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Working Paper Series
Small Businesses and Small BusinessFinance during the Financial Crisis and the
Great Recession:New Evidence from the Survey of
Consumer Finances
Arthur B. KennickellBoard of Governors of the Federal Reserve
System
Myron L. KwastFederal Deposit Insurance Corporation
Jonathan PogachFederal Deposit Insurance Corporation
Current Version: September 2015
FDIC CFR WP 2015-04fdic.gov/cfr
NOTE: Staff working papers are preliminary materials circulated to stimulate discussion and criticalcomment. The analysis, conclusions, and opinions set forth here are those of the author(s) alone and donot necessarily reflect the views of the Federal Deposit Insurance Corporation. References in publicationsto this paper (other than acknowledgment) should be cleared with the author(s) to protect the tentativecharacter of these papers.
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Small Businesses and Small Business Finance During the Financial
Crisis and the Great Recession: New Evidence From the Survey of
Consumer Finances
Arthur B. Kennickell†
Myron L. Kwast‡
Jonathan Pogach‡
This Draft September 29, 2015
Abstract
We use the Federal Reserve’s 2007, 2009 re-interview of 2007 respondents, and 2010 Surveys
of Consumer Finances (SCFs) to study how small businesses owned and actively managed by
households fared during those turbulent years. Even though the surveys contain extensive
data on a broad cross-section of firms and their owners, to the best of our knowledge this is the
first paper to use these SCFs to study small businesses. We find that the financial crisis and the
Great Recession severely affected the vast majority of small businesses, including tight credit
constraints. We document complex interdependencies between the finances of small
businesses and their owner-manager households, including a more complicated role of housing
assets than reported previously. We find that workers who lost their job during the recession
responded in part by starting their own small business, and that factors related to the survival
of a small business are hard to identify. Our results support the importance of relationship
finance to small businesses and the primary role of commercial banks in such relationships. We
find that both cross-section and panel data are needed to understand the complex factors
associated with the creation, survival and failure of small businesses.
JEL Codes: D12, D22, G21, L25, L26 Keywords: Small Business, Entrepreneurship, Great Recession, Credit Constraints † Board of Governors of the Federal Reserve System, Washington, DC. The views expressed are those of the
author and do not necessarily reflect those of the Board of Governors or its staff.
‡ Federal Deposit Insurance Corporation, Washington, DC. The views expressed are those of the authors and do
not necessarily reflect those of the FDIC or its staff.
The authors thank Alicia Robb, our conference discussant, the conference volume editors, and Yan Lee, Philip
Ostromogolsky, and Robin Prager for very helpful comments and suggestions; and Cody Hyman for excellent
research assistance. All errors are the sole responsibility of the authors.
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Small Businesses and Small Business Finance during the Financial Crisis and the Great
Recession: New Evidence From the Survey of Consumer Finances
It is widely understood that small businesses, small business formation and the
successful financing of both are critical components of the U.S. economy and vital to strong
and sustainable economic growth.1 In addition, it is generally believed that small businesses
are, after their start-up phase, relatively dependent on depository institutions, and
especially their “relationships” with commercial banks, for credit and other financial
services.2 Thus, the fates of both established and new small businesses during the recent
financial crisis and the ensuing recession have been of intense interest to policymakers,
practitioners, academics, and the general public.3
This paper uses data from the Federal Reserve Board’s Survey of Consumer Finances
(SCF) in 2007, 2009, and 2010 to examine the experiences of established and new small
businesses owned and actively managed by households during these years. In addition, we
distinguish between small businesses without employees from those with employees. We
believe this is the first paper to present a comprehensive analysis of small businesses during
this period, and the first to use these SCF data on small businesses. Although the SCF has
been used by many researchers since its inception in 1983 to study household finances, we
1 Recent papers supporting this view, but in some cases expressing concerns for the future, include Decker,
Haltiwanger, Jarmin and Miranda (2014), Haltiwanger, Jarmin and Miranda (2013) and Neumark, Wall and Zhang (2011). 2 For a review of the literature supporting this view see Udell (2008).
3 Thus, virtually at the peak of the crisis Congress demanded testimony by Federal Reserve and other officials
regarding the crisis’ effects on small businesses (see Kroszner (2008)). Of course, policymakers’ interest in and concern for small business is far from new – the Small Business Administration was created in 1953.
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know of only one other study that has used its information on small businesses owned and
actively managed by households, and the data used in that study ended with the 1995 SCF.4
The combination of the three surveys provides a new, unique and logically consistent
data set to examine a wide variety of factors that affected small businesses and their
owner’s households before, during, and just after one of the most important periods in U.S.
economic history.
The analysis of the surveys used here has at least four important advantages over
previous work. First, because the SCFs survey households with a focus on wealth and the
sources and uses of income, they are well-suited to evaluate interactions between small
business and household finances. Such interactions have long been considered central to
understanding entrepreneurial activity. Moreover, the SCF allows comparisons of
households that have a small business with those that do not own a small business.
Second, the timing of the surveys allows us to observe small businesses and their owners
just before, during the heart of, and just after the financial crisis and the Great Recession.
Third, the 2009 survey was a re-interview with participants in the 2007 SCF. This panel
structure allows us to study directly how a set of small businesses and their owners were
affected in the heart of the financial crisis and Great Recession. Fourth, the information on
personal businesses collected in the SCF was expanded considerably in the 2010 survey, and
some of that additional information is also available in the 2009 survey. This allows testing
a number of findings of pre-crisis studies and provides a benchmark for future research.
4 See Avery et al. (1998).
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In addition to describing and analyzing small businesses over the crisis and its
aftermath, this paper contributes to four areas of the small business literature:
(1) Distinctions between established and new small businesses,
(2) Interdependencies and other interactions between the finances of small businesses
and those of their owner’s household,
(3) The importance of “relationship finance” for small businesses, and
(4) Determinants of the probability of success, failure and creation of a small business.
By way of preview, we summarize briefly our key findings. The financial crisis and the
Great Recession severely affected the vast majority of both established and new small
businesses. This includes the fact that many firms faced severe credit supply constraints.
While the weak economy was cited as a reason for the actual or expected denial of credit,
causes more internal to the firm, such as credit history and a poor balance sheet, were cited
much more frequently. We find that the interdependencies and other interconnections
between the finances of small businesses and their owner-manager households are
numerous and complex. We identify a variety of measures of small business – household
interconnections. Some, such as a household’s net worth, are common to the literature.
Others, such as indications of a more complicated role of housing assets in small business
finance, are new. Our results indicate that relatively well-educated households and workers
who lost their jobs during the Great Recession responded in part by starting their own small
business. Factors correlated with the survival of a small business during the crisis and the
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Great Recession are, with the exception of a household’s ex ante net worth, problematic to
identify. Our results strongly reinforce the importance of relationship finance to small
businesses, and the primary role played by commercial banks in such relationships. The key
deposit services for small businesses are business checking and savings accounts, and the
core credit services are business lines of credit, business loans and possibly credit cards.
Local banking offices are highly important for small businesses. Comparisons of results
found using cross-section data with those found using panel data indicate that both types of
information are highly valuable for researching the topics this paper addresses. Thus, both
cross-section and panel data are needed to advance our knowledge about household and
small business economics.
The paper proceeds as follows. The next section reviews the extensive small business
literature to distinguish our study and place our work within its context. Section II describes
the SCF small business data, including important differences across the three waves of the
survey we use, discusses limitations when using the SCF data, and compares the SCF small
businesses to those found in U.S. Census reports. This section sets the stage for our
substantive analysis, which proceeds in four parts. Section III uses variables available on
both the 2007 and 2010 SCFs to compare small businesses and the households that own
and actively manage them before and after the financial crisis and the Great Recession. In
addition, households that own and actively manage a small business are compared with
households that do not own a small business. Section IV uses the 2007 cross-section survey
and its 2009 panel re-interview to examine differences between small businesses (and their
owner-manager households) that survived and those that failed during the crisis and
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recession. This section also identifies the key characteristics of households that started a
small business during this period. Section V employs the expanded small business data
collected on the 2010 SCF and the more limited new data collected on the 2009 panel (but
not the 2007 SCF) to investigate a wide range of small business finance topics during these
years. Section VI summarizes the key findings from the previous three sections to provide a
unified narrative of the experiences of small businesses owned and actively managed by
households over the period covered by the three surveys. The section ends with
recommendations for future research and improved data collection.5
I. Literature Review
While the academic literature on small business is huge, virtually all of it predates the
recent financial crisis and the Great Recession. That being said, the literature identified a
number of interrelated issues and principles that help our work. First, small business
research generally distinguishes between established and new small businesses because the
two groups exhibit important differences. These differences derive in part from the skills
required of entrepreneurs versus those needed by the managers of a going concern. But
the differences are also believed to result from a “life cycle” of small business finance and
the likelihood that a business will grow from a start-up to a successful larger firm. Second,
the interdependencies and other interactions between small business and household
finance at both established and new small businesses are typically seen as important but
are still poorly understood. Third, for both groups, the relationships between the firm and
5 Appendix A defines the variables used in this study, and Appendix B provides univariate results that form the
basis for the multivariate correlations discussed in the main text.
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its sources of finance, especially commercial banks, are viewed as critical to the success of a
small business. Indeed, previous research indicates that the importance of relationship
finance is a fundamental difference between small businesses and larger firms that have
access to broader capital markets. Last, the probabilities that a small business will succeed,
fail or be created at all derive from the interactions of many characteristics of the founding
entrepreneur or current owner, the firm itself, the industry the firm is a part of, and the
financial and economic environment in which the firm operates. Each of these topics is
discussed below.6
The life cycle of small business finance
Berger and Udell (1998) reviewed and described the life cycle of small business finance.
Initially a new firm is not only young and small, but its risk characteristics are typically not
transparent to outside investors. Very young firms frequently rely on “inside finance” from
the founding entrepreneur and possibly friends and family members. As the firm grows and
begins to show potential for success, angel and venture capital may become available.
Eventually the business may come to rely heavily on “outside finance” as commercial banks
and other financial institutions become willing to grant lines of credit and loans, public
bonds may be floated, and perhaps public equity markets tapped. Along the way, other
financial instruments such as trade credit, commercial paper and private placements of
6 There are other core small business issues that we do not address. These include the effects of financial (and
especially bank) market structure on small businesses’ access to funds, the macroeconomic importance of small businesses, including their role in job creation and the transmission of monetary policy, and the roles of gender and race in small business formation and success. For discussions of the first topic, see Kerr and Nanda (2009), Black and Strahan (2002), and Petersen and Rajan (1995); for the second, see Decker et al (2014), Haltiwanger et al (2013) and Udell (2008); for the gender and race topic, see Robb and Robinson (2012) and Hurst and Lusardi (2006).
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debt or equity may be used. The fact that the life-cycle paradigm has considerable
empirical support means that it provides a useful guide to researching small business
finances.
Still, Berger and Udell emphasize that the life-cycle paradigm does not fit all small
businesses and should only be used as a rough approximation. A recent paper by Robb and
Robinson (2012) reinforces their point.7 Robb and Robinson find that “in spite of the fact
that these firms are at their very beginning of life, they rely to a surprising degree on bank
debt.”8 However, more consistent with the life-cycle paradigm, Robb and Robinson find
that much of this debt is tied directly to the entrepreneur through a sole proprietorship or
personnel assets used as collateral.
Our study contributes to this debate in several important ways. First, we adopt the life-
cycle paradigm as an organizing principle and test for differences between established and
new firms and for the importance of firm size. Second, we are able to estimate a firm’s
probability of survival over the crisis, controlling for life-cycle characteristics and other
household and firm characteristics. Last, we use the augmented data on small businesses
available on the 2010 SCF to investigate whether some of the key findings of the pre-crisis
literature are supported by post-crisis data.
Interdependencies between small business and household finance
7 These authors use the Kauffman Firm Survey of businesses founded in 2004 to study the capital structure
decisions of small businesses in their initial year of operation. The Kauffman Firm Survey is described in Robb and Robinson (2012) and Robb and Reedy (2011). 8 Robb and Robinson (2012), p. 25.
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The importance of interdependencies and other interactions between small business
and household finance has long been recognized. However, the vast majority of studies
have focused only on the relationship between household wealth and the probability of
starting a new business. Positive correlations are typically interpreted as supporting the
view that liquidity constraints are binding for many start-up businesses, and thus reinforce
the importance of inside finance for small businesses.9
More recent research has challenged the strength of the wealth/small business
formation relationship. In two papers, Hurst and Lusardi (2006 and 2004) find that “Over
most of the distribution of wealth, there is no discernible difference in the propensity to
become a business owner. It is only at the very top of the wealth distribution [top 5
percent] that a positive relationship between wealth and business entry can be found.”10
These authors also “find that both past and future inheritances predict current business
entry, showing that inheritances capture more than simply liquidity.”11
In (to our knowledge) the only study of its kind to date, Avery et al. (1998) documents
that the relationship between small business and household finances is highly complex.12
For example, in partial support of Hurst and Lusardi, Avery et al. find “no consistent
9 Evans and Jovanovic (1989) were among the first to find a positive relationship between individual wealth and
entrepreneurial activity. Holtz-Eakin et al. (1994) found a positive correlation between the probability of starting a business and receiving a recent inheritance. Schmalz et al. (2013), using French data from before the crisis, found a positive correlation between increases in house prices and the probability of starting a small business. 10
Hurst and Lusardi (2006), pp. 1–2. 11
Hurst and Lusardi (2004), p. 319. 12
These authors use data from both the National Survey of Small Business Finances and the Survey of Consumer Finances from the late 1980s through the mid-1990s.
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relationship” between an owner’s wealth and the use of personal commitments (personal
guarantees and pledges of personal collateral) when a small business seeks credit.13
Consistent with Robb and Robinson (2012), they find that personal commitments are
important credit enhancement tools for those small businesses “that rely heavily on loan
financing,” and that “loans with personal commitments comprise a majority of small
business loans, measured in numbers or dollar amounts.”14 Last, Avery et al. “find strong
evidence that personal commitments are substitutes for business collateral, at least for
lines of credit.”15
Our study expands and updates our knowledge in these areas, both during the crisis and
as the economy began to recover. For example, we test the interdependence between
household wealth and the probability not only of starting but also of continuing to run a
small business. Unlike previous studies, we are able to separate the effects of wealth based
on home ownership and wealth that is independent of home ownership. Other variables
that give insight into small business/household interactions include the business owner’s
age, education, partnership status, risk preferences and method of acquiring the small
business, and we examine the importance of credit relationships running from the
household to the small business.
The importance of relationship finance
13
Avery et al (1998), p. 1058. 14
Ibid. 15
Ibid., p. 1059.
11
The third core issue to which our paper contributes is the importance of “relationship
finance.”16 Relationship lending relies primarily on “soft information” about a borrower,
acquired over time by a lender who often has multiple interactions across a variety of
financial services with its customer. Soft information is difficult to transmit both within and
across organizations and, in the case of small business lending, typically includes deep
knowledge of the business’ local market. Indeed, Udell (2008) emphasizes “there is
considerable evidence that relationship lending may be best delivered by community banks,
where soft information does not have to be communicated across locations or hierarchical
structures.”17
While the importance of relationship finance appears well-established, the importance
of local banking offices to small businesses remains controversial. Using data from 1993,
Petersen and Rajan (2002) argued that technological change has altered small business
lending markets, weakening the importance of local offices of credit suppliers and
increasing the physical distance between small businesses and their sources of credit.
Papers that use more recent data challenge this view, albeit with a number of important
subtleties. Using data from 1997 through 2001, Brevoort and Hannan (2006) find that
rather short distances between borrower and lender (two to five miles) still matter for small
business lending, though more so for small banks than for larger organizations. DeYoung et
16
Udell (2008) reviews the large body of theoretical and empirical research on relationship banking as it applies to small business lending. A short and clearly selective list of other important papers in the relationship banking literature includes Schenone (2010), Berger and Udell (2006, 1998, 1995), Berger et al. (2005), Elyasiani and Goldberg (2004), and Petersen and Rajan (1994). 17
Udell (2008), p. 94. Black and Strahan (2002) present evidence that challenges this view.
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al. (2008), using data from 1984 through 2001, find that greater lender/small-business
borrower distances increase the probability of loan default. And, using data through 2003,
Brevoort et al. (2009) find that while some distances have increased for some financial
products (e.g., asset-backed loans) and some small businesses (higher-credit-quality or
more-established firms), “distance increases for relationships involving lines of credit or
multiple credit product types (bundles) were effectively zero” from 1993 to 2003.18
In contrast to relationship lending, most other lending technologies, often called
transactions-based lending, rely more on “hard information.” According to Udell (2008),
hard information, such as financial statements and credit bureau reports, “is easily
quantifiable and easily transmitted within the hierarchy of a large banking organization.”19
Examples of transactions-based lending technologies include financial statement lending,
asset-based lending, leasing, and credit scoring. It is notable that Udell’s list illustrates that
in the real world of commercial lending, there is not a sharp distinction between the lending
technologies available to community, medium-sized and very large banks. Community
banks may have a comparative advantage in relationship lending, but they also use
transactions-based technologies, and vice versa for larger banks.
The data on small businesses in the 2010 SCF allow us to examine relationship banking
issues at the small business level. We can investigate whether some of the findings of pre-
crisis research on relationship banking have held up over this period and establish
18
Brevoort et al. (2009), p. 26. 19
Udell (2008), p. 94.
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benchmarks for future research. Moreover, relationship banking and small business finance
issues not only are important for better understanding the nature of relationship finance,
they also lie at the core of the methodology used by the U.S. Department of Justice and the
federal banking agencies for evaluating the potential competitive effects of proposed bank
mergers and acquisitions.20 Specifically, we contribute to the discussion of three
fundamental concepts. First, because we can identify the type of financial institution a
small business considers its “primary” financial institution, we can assess the continuing
importance of commercial banks, other types of insured depositories, and other classes of
financial institutions to small businesses. Second, while we cannot tell the size of a small
business’ primary financial institution, we do know the distance between a small business
and the nearest office of its primary financial institution. Thus, we provide an update on
the importance of local bank offices and the local offices of other financial institutions.
Third, we can evaluate the continuing importance of credit, deposit and payments financial
services to a small business and the extent to which firms tend to cluster, or bundle, their
use of financial services at their primary financial institution.
Small business survival, failure, and creation
The final issue to which our study contributes is the empirical analysis of small business
survival, failure, and creation. This literature is voluminous, dates to at least the 1930s,
contains both quantitative and qualitative studies, and extends across many countries.21
20
Many of the concepts used in this methodology have been controversial for years. See Kwast et al. (1997). 21
While we cannot review this entire literature here, we do want to place our work within its context. Recent reviews appear in Mach and Wolken (2012) and Balcaen and Ooghe (2006). Other interesting and relatively
14
Studies have identified four broad categories of relevant factors: characteristics of the
founding entrepreneur or current owner, characteristics of the firm itself, characteristics of
the industry in which the firm competes, and the financial and economic environment in
which the firm operates.
Characteristics of the firm’s founder and/or owner that have been found to be
important are that person’s age, education, financial endowment, management experience,
attitude toward risk, access to credit and credit quality, previous experience in starting a
new firm, gender, and race. Firm characteristics found to be important include financial
ratios, age, size, access to credit and credit quality, organizational form, and geographic
location. Industry characteristics that are often considered are broad categories of the type
of industry (e.g., retail, manufacturing, and service), overall growth rates in the industry, the
degree of competition, and the size of the industry.
While each of these factors has been considered in one or more studies, no study has
considered all of the factors. Ours is no exception. However, unlike most previous work, we
are able to include variables in each of the first three categories in our examination of the
probability that a small business survived the crisis. For example, we include the owner’s
age, education, net worth and attitude toward risk, as well as a variety of firm and industry
characteristics.
II. Small Businesses in the Surveys of Consumer Finances
recent work includes Cole and Sokolyk (2014 and 2013), Hunter (2011), Liao et al. (2008/09), Ooghe and Prijker (2008), Strotmann (2007), Cressy (2006), Headd (2003), Honjo (2000), and Everett and Watson (1998).
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This paper uses information from the cross-sectional SCFs in 2007 and 2010 and from
the panel re-interview in 2009 with participants in the 2007 survey. The SCF is
distinguished from other U.S. household surveys by its focus on wealth measurement and
inclusion of an over-sample to provide adequate coverage of very wealthy households.
These characteristics have special utility for this paper. The SCF collects detailed
information on all aspects of wealth, including the closely held businesses that are the
subject of this paper, along with supporting information on sources and uses of income, use
of financial services, and other demographic and attitudinal characteristics. This
information allows us not only to examine important details of businesses, but also to look
closely at the relationship between some key dynamics of businesses and important aspects
of the financial situation of the business owners.
The high-wealth over-sample helps to provide a better representation of some of the
more financially successful business owners. For example, in 2010, 13.3 percent of
households overall had some type of closely held business investment, while the
corresponding figure for the wealthiest 1 percent of households was 75.3 percent. For
households outside the wealthiest 1 percent, the share of business wealth in total
household net worth was 12.1 percent, while among the wealthiest 1 percent the figure
was 37.7 percent. Thus, inadequate coverage of the wealthiest households would likely
affect seriously the ability to analyze personal businesses.
Limitations of the SCF small business data
While the SCF is a rich source of information, its design imposes limitations on our
analysis. For example, the 2007 and 2010 surveys used a lengthy questionnaire to cover the
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affairs of each sample household at a relatively fine level of detail. However, a variety of
concerns required shortening the length of the 2009 re-interview. Consequently, much of
the detail in the regular cross-sectional surveys was suppressed, while the higher-level
architecture framing the questions was retained. For the section of the survey covering
businesses, this meant collapsing the information on all actively managed businesses to a
total of the values of the businesses and the loans of the businesses to, from, or sponsored
by the household. We offset this limitation of the 2009 survey somewhat by adding new
elements to the panel questionnaire to obtain information relevant to understanding the
effects of the financial crisis. For example, whenever a business had been reported in the
2007 survey and the 2009 respondent no longer reported a business, the respondent was
asked what had become of the business. Thus, we can study factors that affected the
survival or failure of these businesses. Conversely, we can identify when a business appears
on the 2009 survey but not on the 2007 SCF. Thus, we can study factors that affected a
household’s decision to create a small business during this period. In addition, questions
were added in 2009 on recent credit experiences and expectations of credit availability. The
2010 SCF incorporated these credit-related questions and added more detailed questions
on the use of financial services by businesses.
The survey design also affects the scope of entities that can be considered in our
analysis. First, not all assets treated as businesses for tax purposes may be reported by SCF
respondents as a business. The only non-negligible exception of this sort in the SCF is
investment real estate. Because some households appear to report such assets as
businesses and some do not, researchers sometimes combine such information to produce
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a more uniform measure of businesses. However, because the information collected in the
survey for real estate investments differs in important ways from that collected for
businesses in the SCF, we do not include such real estate holdings in our analysis.
Households reporting that they own one or more businesses may have an active interest
in running their business or a passive one. It is a reasonable assumption that the active
owner would be more knowledgeable about the operations of its business. As a result, the
SCF collects more detailed information on businesses in which the household has an active
management role. To take advantage of this information, we focus on the set of actively
managed businesses and their owners. In 2010, 12.5 percent of households had at least
one business with an active management role and 1.3 percent had at least one with a more
passive management role (0.5 percent had both).
There is often not a clear distinction between self-employment and business ownership.
Some types of self-employment may not be associated with assets or liabilities that survey
respondents would consider a business. In both the 2007 and 2010 surveys, when
respondents who did not report a personal business answered later in the interview that
they were self-employed, they were asked whether their self-employment was associated
with a business with any net value. This follow-up captures some additional businesses, but
it does not address business structures that have no significant net value. Moreover, the
check on the data is not symmetric in that there may be businesses reported directly that
have no significant net value. In 2007, 74.3 percent of households that reported self-
employment activity by the household head or that person’s spouse or partner also
reported owning a personal business; in 2010, the proportion dropped to 70.6 percent.
18
In principle, the 2007 SCF collected detailed information on up to three actively
managed businesses and the 2010 survey collected such information for as many as two;
any remaining actively managed businesses were recorded as summary information. In
practice, it appears that it was common in both surveys for respondents to combine
multiple businesses effectively operated as a single business but that retained a degree of
legal separation for tax or other reasons. The interview questionnaire instructions allow
this way of reporting. The advantage of this approach is that the business as reported is
more likely to reflect the business in a functional sense; the disadvantage is that the
business described may not be a single legal entity. It is impossible to give a precise
estimate of the extent to which multiple businesses might be combined in this way in the
SCFs.
Some closely held businesses are large, sometimes as large as well-known publicly
traded firms. Such large firms almost surely look and behave differently from smaller and
more entrepreneurial firms. To avoid potential biases and sharpen the focus of this study,
we restrict the set of businesses considered to those with fewer than 500 employees. In
the 2010 SCF, only 0.8 percent of primary businesses were larger than this size.
We adopt one other restriction on the set of businesses considered. The SCF includes
farm businesses along with other types of businesses, but when a farmer also lives on some
part of the property farmed, which is often the case, the information available is less
straightforward to use than is the case for other types of business. For example, separating
the value of land farmed from the associated residence and its mortgages or loans typically
requires strong assumptions about what should be attributed to services purely related to
19
housing. An even more difficult problem is the proper treatment of financing options and
government incentives that apply entirely or largely to farmers. Our view is that combining
farms with other types of businesses in the SCF would risk substantially reducing the clarity
of our results. We therefore do not include as a business any farm that is also the primary
residence of the household. In 2010, 0.8 percent of the SCF households had a farm
business on a property where they lived.
Perhaps the most important limitation of the SCF for business analysis is that while the
survey is designed to gather data on the businesses owned by households, it is not designed
to be representative of the population of businesses. Only in the case of ownership of a
sole proprietorship or other business with no owners outside the survey household do the
household and business units coincide statistically. To realign the survey to represent the
universe of privately held businesses, it would be necessary to adjust the household weight
associated with each business owner, accounting for the business ownership share. In
addition, this adjustment would need to be performed separately for each business a
household owned. Unfortunately, such an adjustment is not possible for the SCF, because
ownership shares are only collected for the set of actively managed businesses and only on
the first three (two) businesses for which detailed information was collected in 2007 (2010).
To address this limitation, this study focuses on the first actively managed business
reported by respondents in the 2007 and 2010 surveys, which should be the largest or most
important one for the household. Among this set of “primary businesses,” 80.2 percent
were fully owned by the household and 7.2 percent were half-owned. In addition, our set
20
of primary businesses includes 72.4 percent of the total value of small businesses in the
2007 SCF, and 68.9 percent of the total value in 2010.
For all these reasons, the results reported here do not describe the full universe of
closely held businesses, but rather the universe of primary, actively managed, nonfarm
business interests, weighted by the population of owners. However, the available data
suggest that this more limited approach should be highly representative of the larger
universe.
Comparing SCF and U.S. Census Data
Estimates of the U.S. Bureau of the Census reported in the Statistics of U.S. Businesses
(SUSB) and a series on non-employer businesses provide some basis for examining the
degree of coverage of the universe of all small businesses using the definition of small
businesses we have developed from the SCF for this paper. According to Census, there
were about 27.8 million non-employer (no employee) businesses in 2007. Estimates based
on our definition derived from the principal business owned by the household indicate that
there were only about 5.5 million such businesses because of our more narrowly focused
approach.
Several factors may explain the large difference. First, as noted earlier, many self-
employed people do not report in the SCF that they own a business. If all households with a
self-employed head or spouse/partner and no reported business are treated as having a
non-employer business, the SCF estimate rises to about 10.8 million. Second, as noted
earlier, some households have real estate holdings that are formally organized as a business
but reported as directly owned real estate in the SCF. Including all of the properties from
21
which the household is known to have received any income as non-employer businesses
raises the total to about 18.8 million. However, this augmentation with real estate holdings
almost surely overstates the possible number of unreported non-employer businesses in
the SCF, not least because ownership of income-producing real estate is often shared and
thus may be double-counted. Third, and possibly most important, the Census estimate of
non-employer businesses is based on business tax returns filed at any point during the year.
If there is significant flux in the existence of this smallest category of business, the wider
window of the Census estimate would capture more short-lived businesses than the SCF,
which is based on the state of the household’s assets as of the time of the interview.
SUSB estimates of the number of employer (one or more employees) businesses are
made using the Census Business Registry, which purports to be a list of all existing U.S.
businesses with employees. The Census estimates that there were about 6 million
employer businesses with fewer than 500 employees in 2007. Estimates with our definition
indicate that there were about 8.1 million households with such businesses as their primary
business. Part of the difference in these estimates may be the result of the inclusion of
more than one household member among the total number of people working for the
business. Assuming that any household head or spouse/partner who works in the
household’s business is not an employee, the SCF estimate of the total number of employer
businesses with fewer than 500 employees falls to about 6.9 million. Adjusting the SCF
estimate for the share of the business that each household owns reduces the number of
household businesses with fewer than 500 employees to about 6.1 million.
22
On balance, these comparisons indicate that while—for a variety of logical reasons—the
SCF and Census data do not match well for no employee small businesses, the two data
sources compare closely with respect to numbers of employer firms. For this reason, much
of our analysis separates no-employee small businesses from firms with at least one
employee.
Tables B7 and B8 in the appendix further compare the distribution of SCF employer
businesses by our definition with SUSB data by industrial sector and by firm size for 2007.
Given the small sample size of the SCF, there are some differences in the patterns shown by
the two sources. Overall, however, the distributions are similar.
Adjustments for statistical concerns
Like any survey, the SCF is subject to potential error because of the small sample of
population interviewed. In addition, some households selected into the survey do not
participate, making it possible that the characteristics of participants might differ from
those of non-respondents in ways that induce bias in the statistics we report. The SCF
addresses these problems through weighting. Non-response adjustments tailored to the
survey help to mitigate the effects of non-response. A replication method is used to
estimate variability caused by sampling; many pseudo-samples are selected from the set of
completed cases, and the full set of weighting adjustments is made for each such pseudo-
sample. The variability of estimates across calculations using each of the replicate weights
serves as an estimate of the range of variability of estimates as a result of sampling.
In addition to the non-respondents, some respondents fail to give answers to all the
questions asked. The SCF uses a multiple imputation to estimate the distribution of the
23
missing data. Under this method, multiple values for a missing item are randomly drawn
from the distribution of the value, conditional on the observed information. Variability of
estimates across different sets of such draws represents the added uncertainty as a result of
having missing information.
III. Small Businesses in 2007 and 2010
This section uses variables available on the 2007 and 2010 SCFs to examine small
businesses and the households that own and actively manage them before and just after
the financial crisis and the Great Recession. In addition to analyzing separately 2007 and
2010, we follow the literature’s convention of distinguishing established from new small
businesses.22 As noted in the previous section, we also distinguish firms with no employees
from those with at least one employee.23 While we only report and analyze multivariate
results here, Appendix B (Univariate Analysis) provides our univariate results. This section
also describes some key characteristics of SCF small businesses not discussed in Section II.
Households that own and actively manage a small business
22
Established businesses are defined as small businesses that are more than 3 years old or that were acquired by the household more than 3 years previously. While there is no standard definition of a new small business, many studies use between two and four years and thus our choice of three years seemed “reasonable.” See Everett and Watson (1998). Moreover, the tri-annual nature of the SCF means that there is negligible overlap in the population of our new small businesses. In 2007, in our sample of 1,137 small businesses, 82 percent of the SCF’s small businesses met this definition of established and 18 percent were new; in 2010, of a total sample of 1,536 small businesses, the percentages were 85 percent and 15 percent respectively. 23
Sample size limits prevent us from combining the two concepts.
24
Tables 1A and 1B provide the results of multivariate logit regressions that estimate the
probability that a household owns and actively manages a small business.24 Table 1A
separates households into those with an established small business (left panels) and those
with a new small business (right panels) in a given year, 2007 and 2010. Table 1B separates
households into those whose small business had no employees (left panel) from those with
at least one employee (right panel), again by 2007 and 2010. The right-hand-side variables
in the regressions reported in the (1) columns of both tables are the same as those in our
univariate analysis, with three exceptions. Only income including that from the small
business and net worth including small business equity are included, and the ratio of home
equity to total net worth proxies for home ownership is included as well.25 The additional
specifications presented in the (2) columns will be discussed shortly. In all cases, the
reported coefficients are marginal effects. We use the conventional definition of a marginal
effect: the impact of a unit change in a right-hand-side variable on the estimated probability
when all right-hand-side variables are measured at their mean.
Looking first at the (1) columns for households with an established small business
(versus those with no small business) in 2007, all but two of the right-hand-side variables
are statistically significant at the 5 percent level or better. Interestingly, neither the
household’s income nor its risk preference variable is significant.
24
All data analyses in this study use SCF analysis weights as described in section II’s “Adjustments for statistical concerns” sub-section. 25
Inclusion of both this ratio and a homeownership indicator variable led to substantial multicollinearity. All dollar values enter in log form in these and subsequent regressions. As explained in more detail in Appendix B, all dollar values are in 2007 dollars.
25
The results for 2010 are similar but not quite the same. In 2010 a household’s age,
education, and risk preference were insignificant, but now its income was significantly
negative. On balance, ceteris paribus, in both years a household was more likely to have an
established small business if it had higher net worth, if less of its net worth were in home
equity, if it were partnered, and if it had access to credit.26 In 2007, younger and less
educated households were more likely to own and actively manage an established small
business, but these variables were not significant in 2010.
Results for households with a new small business (right panel of table 1A) are similar to,
but also differ in interesting ways, from those for households with an established small
business. Thus, ceteris paribus, and as was the case for households with an established
small business, in both years a household was more likely to have a new small business if it
had higher net worth, if it were partnered, and if it had access to credit (although the level
of significance in 2010 is a very weak 12 percent). However, in contrast to households with
an established firm, in both years a household was more likely to have a new small business
if it were younger, more educated, and if it were less risk averse. On balance, these results
support our and the literature’s emphasis on separating established and new small
businesses. It is interesting that in both years, the household’s income and the ratio of its
home equity to total net worth were not correlated with the probability of owning and
actively managing a new small business.
26
We use a conservative definition of credit access. The household must have a loan from an insured depository other than a credit card or an educational loan.
26
Turning to table 1B, we again focus initially on results in the (1) columns. Overall, the
logit that separate households into those firms that have no employees versus those that
have some employees are much more consistent across the two groups and across both
years than were the regressions that separated households with established versus new
firms. Thus, ceteris paribus, in both groups and both years, a household was significantly
more likely to have either a small business with no or some employees if it were younger,
had greater net worth, a smaller ratio of home equity to net worth (except no-employee
households in 2007), were partnered, had access to credit (again except no-employee
households in 2007), and if it were less risk averse (except employee households in 2007).
As was true in table 1A, a household’s income was either negatively correlated or
uncorrelated with the probability that it has a small business. Interestingly, the fact that all
of the negative correlations with income in both tables occur in 2010 may suggest some
causal impact of the recession, a topic we will discuss in more detail. In addition, the
disparate correlations of education with the probability of owning a small business suggest
an important distinction. More education increased the probability of owning a new small
business or a firm with no employees, but it was either unrelated or negatively correlated
with the probability of owning an established small business or a firm with employees.
On balance, the results of the two tables suggest that while separating households into
those with established versus new firms is generally critical for researchers (and
policymakers), distinguishing households whose firms have no employees versus some
employees may be less critical.
27
The statistically negative or zero correlation of household income with the probability of
having an established or a new small business or one with no or some employees is
unexpected and inconsistent with our univariate results. A straightforward interpretation is
that once we control for other important factors, such as net worth, income fades in
importance for understanding which households own and actively manage a small
business.27 Alternatively, it may be that a household that started a small business had
relatively unstable incomes. It may also be that important conceptual differences between
the income of households with no small businesses (e.g., relatively more wages) and the
income of those with a small business (e.g., relatively more unrealized capital gains) confuse
the interpretation of the income coefficient. In addition, Slemrod (2007) provides strong
evidence that underreporting of business income for tax purposes is substantial among
households that own small businesses.28 Thus, our multivariate results for household
income may in part reflect the underreporting of income by survey households. On
balance, while all these factors probably play some role, we lean toward emphasizing the
effects of the Great Recession, subject to the caveat that our income results seem worthy of
additional research.
The generally negative correlation of a household’s ratio of its home equity to total net
worth with the probability that it owns and actively manages a small business is interesting
and provocative, especially because previous studies have been unable to construct such a
variable. On the one hand, our results may merely reflect the conventional wisdom that
27
The negative coefficient on income is robust to a variety of model specifications. 28
Households are encouraged to use tax records to assist their responses to the SCF.
28
households with a small business tend to use home equity as collateral for mortgage loans
that support their business, thereby driving down their ratio of home equity to net worth.
On the other hand, our results may also suggest the new and perhaps provocative
suggestion that, ceteris paribus, a dollar of net worth in home equity may be less valuable
to a household that has a small business than is a dollar in other, possibly more liquid forms
of net worth.
To test these possibilities, we estimated logit models, reported in the (2) columns of
table 1A and 1B, that substitute two new right-hand-side variables for the ratio of home
equity to net worth. “House” is the log dollar value of a household’s housing assets, and
“Mortgage” is the log dollar value of any mortgages for which those housing assets are the
collateral. The column (2) results for these variables are generally strongest for households
with established firms and those whose small business has some employees. These
estimations suggest that (a) conditional on having housing assets, tapping into home equity
via any type of mortgage is positively associated with the probability of having a small
business; but that (b) conditional on the amount of those mortgages, holding a larger
proportion of total net worth in housing may be either a negative or neutral signal of small
business ownership.29 The first result is consistent with the conventional wisdom that many
small business owners use their home as collateral for loans that support their business.
However, the second result is consistent with our new conjecture that other forms of net
worth at the margin may be more valuable to the small business owner. In either case, we
29
This interpretation is supported by all of the results except those for households in 2007 and 2010 whose small businesses have no employees.
29
believe these results warrant further research on the interdependencies and
interconnections between home ownership and small business finance.
Key characteristics of SCF small businesses
Turning to the small businesses themselves, tables 2A and 2B display six key
characteristics of small businesses owned and actively managed by households in 2007 and
2010. Table 2A separates established from new firms, while table 2B distinguishes between
small businesses with no employees and those with a least one employee. Because it is
clear that the means of the variables are often strongly affected by observations in the
upper tail of a given distribution, most of our discussion will focus on the median of a given
variable.
Each of the four measures of size in table 2A suggests that the established small
businesses in the SCF are small. In both years, the median number of employees is only one
and even the 90th percentile is a modest 14 employees. Median annual income (profits) in
2007 is only $41,000 based on median sales (revenues) of $119, 000, and the median value
of the firm is just $110,000. In addition, it is clear that the Great Recession had a substantial
effect, with median real income (business income) falling 51 percent and median total
revenues (business sales) by 33 percent between 2007 and 2010.
Except for the median number of employees (one), the size measures of new businesses
are, as expected, much smaller than those of established firms. In 2007, median profits at
new small businesses were a mere $3,500, median total revenues (business sales) only
$15,000, and the median value of the firm (business value) was only $22,000. The first two
numbers decline to a tiny $500 and $7,000 in 2010, although reported median firm value
30
holds fairly steady at $21,000. Moreover, while the numbers of employees at the 25th and
50th percentiles were unchanged across the two years, the number of employees at the 90th
percentile declined by 56 percent. Taken together, these patterns indicate that the
recession produced a shift toward smaller firms among new small businesses, a pattern not
so evident among the established firms, where the number of employee measures
remained unchanged. This result for new firms is consistent with the view that many
workers who lost their job during the recession formed their own small business, a
hypothesis we investigate more deeply in section IV.
The last two rows of each panel of table 2A provide our first glimpse of the
interdependencies between small business and household finance. In both years, the
indicator for whether a household has either made or guaranteed a loan to its small
business is significantly smaller at the established small businesses than at the new firms.
Moreover, the percentages are stable across the two years at both sets of small businesses.
On average across both years, about 13 percent of established small businesses had either a
loan from or a loan guaranteed by the owner-household, while about 20 percent of new
small businesses had such a loan or guarantee. In another sign of the recession’s effects
(here lower sales for most of the distribution), the ratio of the value of this loan or
guarantee, when one existed, to the firm’s total sales rose substantially from 2007 to 2010
across all four measures of this ratio’s distribution at the established small businesses.
The data in table 2B reinforce the impressions provided in table 2A. The three size
measures continue to show that the small businesses in the SCF are typically small, and the
declines in median income and sales between 2007 and 2010 were mostly severe. In both
31
years, firms with some employees were significantly more likely to have a loan or a loan
guarantee from their owner-manager household. This likelihood remained constant from
2007 to 2010 at the small businesses with employees, but fell significantly at the firms with
no employees. Last, all of the firm age measures remained stable across the two years,
indicating that, at least along this dimension, the two cross-sections were similar.
Table 3 categorizes established and new small businesses (top panel) and firms with no
employees versus some employees (bottom panel) into six broad industry classifications.
This and the subsequent tables in this section separate firms into three groups based on the
number of employees. The boundaries of the groups were defined to reflect substantive
differences in the sample (e.g., the large proportion of businesses with no employees) and
to ensure a substantial number of firms in each employee group.
It is clear that for both established and new small businesses, for all three size classes,
and for both years, the “Professional Services” category dominates with between 36
percent and 51 percent of the firms. Indeed, among all five categories of firms, the sum of
the “Professional Services” and the “Lower Technical Services” categories is over 50 percent
of the firms in all of the ten possible cells. Thus, service industries, which include
professional services and lower technical services, dominate the sample. Still, there are
substantial percentages in all of the industrial classifications, including the heavier
industries of mining and manufacturing. Put differently, the SCF samples represent a broad
cross-section of American small businesses. Overall, the data suggest a move toward
professional services from 2007 to 2010. This perhaps reflects in part the relatively high
32
rate of job loss by certain white-collar workers during the recession, a suggestion we
investigate more deeply in section IV.30
Table 4 categorizes the small businesses by ownership structure, using the same panel
structure as table 3. In both years, sole proprietorships dominate all categories except the
largest firms in 2010, where Subchapter S is the most oft-chosen organizational structure.
Sole proprietorships are especially prevalent among smaller firms, but their percentage is
about the same in both years across new and established small businesses. As firms grow
but not necessarily become more established, corporate and partnership structures become
more common. Despite these broad patterns, it is clear that small businesses chose a
variety of ownership structures.
How households acquire their small business
The final table in this section describes small businesses owned and actively managed by
households according to how they were acquired by the household. The data in table 5
indicate that the vast majority of small businesses, typically over 75 percent, were started
de novo by the household. However, substantial percentages of small businesses, especially
those with three or more employees, were purchased by the household. Relatively few—
typically less than 5 percent—were inherited.
Given the interest in the role of inheritances in the small business literature, we looked
closely at the characteristics of households (and their small business) that inherited their
established small business versus households that acquired their established business in
30
Autor (2010) documents that job losses during the Great Recession “have been far more severe in the middle-skilled white- and blue-collar jobs than in either high-skill, white-collar jobs or in low-skill service occupations” (p. 2).
33
another way.31 On average, households that inherited their business had greater net worth
(including equity in the small business), higher income (including business income), a
smaller percent of their total net worth in home equity, and a higher level of risk aversion.
In addition, small businesses that were inherited tended to be larger and less likely to have
a loan that came from or was guaranteed by its owner-household. While most of these
differences between the two sets of households and the two sets of small businesses seem
reasonable, they suggest that the role of inheritances remains an interesting area for future
research.
IV. Small Business Survival, Failure and Creation From 2007 through 2009
This section uses the 2007 SCF and its 2009 panel re-interview to examine differences
between small businesses that survived and those that failed during the financial crisis and
the subsequent Great Recession and to attempt to identify the key characteristics of
households that started a small business during this period. As was the case in Section III,
we present and analyze multivariate tests here; supporting univariate tables and discussion
are given in Appendix B.
Survival and Failure
The definition of small business failure is not straightforward. According to Everett and
Watson (1998), the literature has used five basic measures: (1) discontinuance of
ownership of the business, (2) discontinuance of the business itself, (3) bankruptcy, (4)
businesses that were sold or liquidated to prevent further losses, and (5) businesses that
31
These results are not shown in a table, but are available on request from the authors.
34
simply could not “made a go of it.”32 The 2009 SCF asks a household if its small business
“went out of business” between 2007 and the survey and the re-interview date allow us to
exclude from this definition businesses that were sold, went public or were transferred to
another family member. Thus, our definition of failure most closely resembles the idea of
failure as discontinuance of the business, not simply a change of ownership or limited only
to bankruptcy.33
The probability of survival
Table 6 presents logit regression results that estimate the marginal effects of a wide
array of right-hand-side variables on the probability that a firm would survive from 2007 to
2009. The independent variables are based on factors identified in the literature (see
Section I) and discussed in Appendix B. The table reports separate regressions for the
pooled sample of all small businesses, and for established, new, no-employee, and firms
with at least one employee. In each case, the logits estimate the relationship of variables
whose values were observed in 2007 with the probability that a small business would have
continued to survive in 2009.
Our primary conclusion from table 6 is that the factors correlated with the probability
of small business survival are at best poorly understood, at least for the recent crisis and the
32
Everett and Watson (1998), p. 374. 33
Of the total number of business terminations in the 2009 SCF, 83 percent met our definition, 15 percent were sold or went public, and 2 percent disappeared for other reasons such as a divorce settlement.
35
Great Recession.34 Very few of the marginal effects are statistically significant, even in the
regressions with the largest number of observations. On balance, these results are a
challenge to several conventional views of what determines small business survival or
failure and thus strongly suggest that this is an area that warrants additional research.
That being said, clues to where research is most needed, and where it is less so, can be
gleaned from an attempt to extract patterns from the results for this period. A household’s
net worth is the variable most consistently correlated with the probability of a firm’s
survival. As in previous studies, ceteris paribus, higher net worth was generally correlated
with an increased probability of survival. Older owner-managers were sometimes
associated with increased chances of survival, although the under 35 years of age indicator
variable has the strongest marginal effect of the three age indicators that are significant.
There is evidence that a partnered household was helpful, especially for new firms. Ceteris
paribus, larger businesses appear to have had a greater chance of survival, although the
distinction between no-employee and some employees firms does not seem to be
important in this regard. Established firms with a loan or a loan guarantee from the
household may have had a higher probability of survival (again reinforcing the importance
of household and small business finance interdependencies), as did a firm with employees
that was over five years old. And, new businesses and firms with employees that were not
part of either the “Professional Services” or the “Mining and Construction” industry
appeared more likely to survive. Higher-income households were associated with a lower
34
In contrast, the univariate comparisons in Appendix B suggested stronger conclusions.
36
probability of survival at new and no employee small businesses. This curious result
reinforces our earlier suggestion that the role of household income in understanding small
business finance deserves further research.
Perhaps the variables that are never correlated with the probability of survival are as
interesting as those that are correlated sometimes. Homeownership, education, access to
credit, and organizational form are never significant. In addition, a household’s degree of
risk preference is only significant in the employee regression, where lower risk aversion is
associated with an increased probability of survival. All of these results challenge either the
conventional wisdom or the existing literature.
Creation
Our discussion of some of the characteristics of small businesses in the SCF conjectured
that workers who lost their jobs during the recession may have responded in part by
starting their own small business. We now pursue this hypothesis more deeply using the
2007 SCF and its 2009 panel re-interview. Using the two surveys, we can identify
households that did not have a business in 2007 but started a business between these two
years that survived at least until 2009. We compare their characteristics with those of
households that neither started nor owned a business during the same period.
The probability of creation
37
Table 7 presents the results of two sets of logit regressions. The first set, contained in
the “Panel” column, relates the probability that a household would start a new small
business between 2007 and 2009 to the variables in univariate table B6. With the exception
of the “Unemp 12 Mo 2009” variable, all of the right-hand-side variables are 2007 values of
a given variable. Thus, this logit is “forward looking” in that it estimates the relationship
between the current values of the right-hand-side variables and future business creation by
the household. The second set, contained in the next two columns, replicates as well as we
can the cross-section regressions for new small businesses reported in the (1) columns of
table 1A. Thus, these logits are “backward looking” in that they estimate the relationship
between the current values of the right-hand-side variables and past business creation by
the household. Put differently, both sets of regressions investigate what household
characteristics are associated with the probability that a household will start a small
business, but each approaches the issue from a different direction.
Looking first at the forward-looking panel regression, households were, ceteris paribus,
more likely to start a small business during the heart of the crisis and the Great Recession if
in 2007 they were more educated, had higher income, had access to credit, and if the head
of the household, had been unemployed at any time in the 12 months before the 2009 SCF.
However, none of the other right-hand-side variables are statistically significant, including,
perhaps most notably, a household’s net worth.
Turning to the backward-looking cross-section regressions, our first observation is that,
using the 2009 re-interview data, we cannot replicate the regressions reported in table 1A.
This is because, as was discussed in Section II, the 2009 SCF did not collect all of the data
38
collected in 2007 and 2010. However, we can come close: the first seven right-hand-side
variables given in table 7 also appear in table 1A.
Of the 14 coefficients listed for these variables in the two cross-section regressions, 12
are both the same sign and statistically significant as in table 2A. Thus, the cross-section
regressions in table 13 tell essentially the same story as the cross-section regressions of
table 2A.
When we compare the panel and cross-section results, the consistency between the two
approaches is seen to be problematic. Of the seven variables common to all three
regressions, only education is the same sign (positive) and statistically significant in all
three.
A household’s income is positive and significant in the panel regression of table 7, but
insignificant in both cross-sections. On the one hand, this asymmetry may reflect the logical
possibility that ex ante households with more income are more likely to start a small
business because they have the cash flow to do so, but realized income is irrelevant for
households that have started a small business in the previous two or three years. On the
other hand, the asymmetry may merely reflect tax avoidance or the fact that we do not
understand the interrelationships between a household’s income and its willingness to start
a small business. Given the difficulty we have had in this study interpreting the role of
income, we think these results deserve more research in this area.
Perhaps the most interesting new results in table 7 are those for the unemployment
history variables. Unemployment by the head of household in the 12 months before the
crisis (2007) is uncorrelated with the probability that the household will start a small
39
business in all three regressions. However, a household head’s unemployment status in the
12 months prior to the 2009 SCF is, ceteris paribus, positively correlated with its probability
of starting a small business in both the panel and the 2009 cross-section regressions. Thus,
both sets of regressions support our conjecture that unemployment during the crisis and
the Great Recession prompted some households to start a small business. The fact that
unemployment prior to the crisis is irrelevant in the 2007 cross-section suggests that the
role of unemployment in small business creation may have been unusually strong during
the crisis and the Great Recession. However, before reaching this conclusion, more
research is warranted.
Our comparisons of these two sets of model results lead us to one more conclusion:
both cross-section and panel data are highly valuable for analyzing these types of issues and
other topics in household and small business economics. The 2009 panel SCF was collected
as a result of a financial crisis and the ensuing Great Recession, but we believe the analyses
presented here have more than proved that panel data should be collected on a more
regular basis. Only panel data offer a reasonable hope of distinguishing changes as a result
of changed circumstances from changes caused by composition effects or shifts within
groups.
V. Small Business Finance in 2009 and 2010
This section uses the expanded small business data collected on the 2010 SCF and the
smaller number of equivalent items included in the 2009 re-interview to investigate small
business finance topics during these years. We begin by discussing small business access to
40
credit in 2009 and 2010, and conclude by describing the types of financial institutions and
broader financial services used by small businesses using data available only in 2010.
Access to credit
Table 8 characterizes established and new (top panel) and no-employee and some
employees (bottom panel) small businesses according to a broad definition of their access
to credit in 2009 and 2010. Each panel contains five measures of credit availability. The
first row gives the percent of small businesses that applied for credit, and the second the
percent of these firms either fully or partly denied credit. The combination of these rows
provides a conventional view of credit availability. The third row provides a less
conventional perspective by showing the percent of small businesses that wanted credit but
did not apply because they expected to be denied even though they had not been denied
credit in the previous five years.35 The fourth measure is the sum of rows 1 and 3, and
provides a measure of total demand, i.e., the percent of small businesses that wanted credit
whether or not they applied. Row 5 gives the percent of row 4 small businesses (i.e., those
that wanted credit) whose credit needs were not fully met.
Starting again with established firms and first looking at rows 1 and 2, the conventional
measures of credit access, while the percent of firms that applied for credit is remarkably
stable across 2009 and 2010, the percent that were denied rose substantially in 2010 (from
11 percent to 20 percent). However, the other measures of credit access suggest that credit
35
Thus we exclude from this measure small businesses that might not be considered creditworthy because they had been denied credit in the recent past.
41
conditions did not change much for established small businesses between the two years.
The percent of firms that wanted credit but did not apply because they expected to be
denied actually fell slightly. Meanwhile, the percentage of small businesses wanting credit
and the percentage of small businesses that had unfulfilled credit needs increased by small
amounts.
Turning to the new firms, it is clear that credit supply constraints were more severe for
new than for established small businesses, especially in 2009. For example, in 2009, 60
percent of new firms said their credit needs were unfulfilled while 36 percent of established
businesses said this was the case. This difference narrowed in 2010, but this percentage
was still substantially larger at the new firms. However, the percent of new firms that were
fully or partly denied fell by almost 16 percentage points between 2009 and 2010. Total
demand for credit (line 4) fell from 40 percent to 25 percent of firms, and the percent of
firms that said their credit needs were unfulfilled fell from 60 percent to 46 percent. On
balance, these data suggest that credit access for new firms actually improved between
2009 and 2010.
A different perspective is provided in the bottom panel of table 8. The percent of firms
applying for credit increased substantially with firm size in both years while the percent of
these firms that were fully or partially denied hovered around 20–25 percent for all size
classes in both years. The percent of firms that wanted credit but did not apply because
they expected to be denied increased with firm size in both years. Thus, total demand for
credit (line 4) increased substantially with firm size, consistent with the partial demand
42
reflected in line 1. Importantly, the percent of small businesses that said their credit
demands were unfulfilled declined substantially with firm size in both years. Thus, credit
conditions were unsurprisingly tighter for the smallest firms. However, credit conditions
appeared to improve, especially for the smallest firms, in 2010. The percent of no-
employee firms that said their credit needs were unfulfilled fell from 59 percent in 2009 to
45 percent in 2010.
Reasons given for actual or expected credit denial
Both the 2009 and 2010 SCFs asked respondents to identify the reasons for either being
denied (given the small business applied for credit) or expecting to be denied credit (given
the small business did not apply for credit). Since owner-managers were not prompted with
possible reasons, the survey recorded a large variety of open-ended responses that were
classified using a common coding framework. However, respondents were allowed to give
only one, and presumably the most important, reason for actual or expected denial. We
have further aggregated the reasons given into eight categories, and the percentages of
small businesses identifying a reason in each of these categories are displayed in table 9. In
both years, the reasons given range from primarily internal factors such as credit history
and a poor balance sheet to external causes such as a weak economy and government
regulation. Because of sample size limitations, table 9 does not separate firms into
established, new, or by size.
Several interesting patterns emerge from table 9. In both years, some type of “credit
history” issue is typically the dominant reason given for either the denial or expected denial
43
of credit.36 Credit history is closely followed by reasons associated with the type or size of
the business37 or business viability (especially in 2009) for why credit was denied.
However, in both years a “poor balance sheet” is also a major reason given for the actual
and the expected denial of credit. Indeed, except for a “weak economy,” cited somewhat
frequently for the expected denial of credit in both years, the other reasons listed in table 9
are of minor importance compared with the first four reasons. For example, “government
regulation” is rarely cited as a reason. On balance, the data in table 9 suggest that reasons
primarily internal to the firm are by far the most common factors cited by small businesses
for their actual or expected denial of credit over these two years.
Relationship finance
As discussed in our literature review, the relationship between a small business and its
financial institutions, especially its commercial bank(s), has been a core concern of small
business finance research. In addition, some of the primary issues in relationship finance,
such as the role of commercial banks versus other financial intermediaries and the
importance of local versus nonlocal banks, are central to the methodology used by federal
agencies in their antitrust analysis. The 2010 SCF allows us to examine some of the most
important issues identified in this research and relevant to policy analysis.
36
Small business owners appear to have difficulty separating business from personal credit history and thus we combine these reasons into one generic credit history category. The evident difficulty of separating business and personal credit history supports the view that for many business owners, household and business finance are closely intertwined. 37
This included reasons such as the small business was too small, a “bad fit” or the “wrong type.”
44
Table 10 provides several perspectives on the importance of credit relationships in 2010
for established versus new small businesses (top panel) and firms with no employees versus
those with some employees (bottom panel). The first four rows of each panel provide a
short-run view by focusing on relationships that existed over the previous year. Rows 5
through 8 provide an intermediate-run perspective by examining relationships over either
the previous five years or since the business came into existence.
The short-run importance of credit relationships with commercial banks, often viewed
as having a comparative advantage in supplying credit to small businesses, is explored in
rows 1 and 2 of each panel. The first row reports the percent of the “primary” small
businesses owned and actively managed by a household that at some point over the
previous year had a business loan, a business line of credit, or a personal loan used for
business purposes with a commercial bank, in all cases excluding credit cards. Looking first
at the top panel, almost 20 per of the established firms and 17 percent of the new firms had
such a bank credit relationship in 2010. Adding credit cards (CC in table 10) to the mix
essentially doubles these percentages for both groups. Turning to the bottom panel, it is
clear that the importance of bank credit relationships increases with firm size. Some 35
percent of the largest firms report such a relationship, but only 9 percent of the no-
employee firms do. Credit cards appear more important for the smallest firms—the
percentage reporting a credit relationship triples for no-employee firms but only rises by a
factor of 0.6 percent at the largest small businesses.
45
While our results indicate that business credit cards are an important part of a small
business’ banking relationship, we cannot tell if they are used primarily for credit or
transactions purposes. However, using the Kauffman Firm Survey, Robb and Robinson
(2012) found they are very important for transaction purposes, much less so as a source of
credit. Using the National Survey of Small Business Finances, Mach et al. (2006) found that
while a substantial percent of small businesses use credit cards, it is unclear how important
they are as a source of credit. Whatever the primary role of credit cards for small
businesses, our results are consistent with the conventional wisdom that commercial banks
are an important source of credit for small businesses.
Rows 3 and 4 add “other” external sources of credit to the definition of a credit
relationship. Such sources include other types of insured depositories (e.g. savings banks
and credit unions) and nonbank financial institutions (e.g. finance companies and mortgage
banks). Comparing rows 1 and 3 in both panels shows that while nonbank sources
contribute to the supply of credit for small businesses, on balance nonbanks appear
(consistent with the conventional wisdom) to be a modest source of funds. For example,
among the established firms, the percent reporting a credit relationship rises from 20
percent to 24 percent, and among the largest firms the percent rises from 35 percent to 40
percent. Consistent with the life cycle theory of small business finance, the role of nonbank
sources at new businesses is more substantial than at the established businesses. Row 4 in
the bottom panel reinforces the increasing importance of credit relationships as a firm
grows. Some 58 percent of the largest firms report a short-term credit relationship, but
only 27 percent of the no-employee businesses do so.
46
Rows 5 through 8 give some insight into the importance of intermediate-run credit
relationships. Because the questions behind these tabulations do not distinguish bank from
nonbank sources of funds, the responses build on the bank and nonbank relationships as
defined in row 3. Comparing rows 5 and 3 in both panels, it is clear that a longer-run
perspective increases the importance of credit to small businesses. For example, among the
established firms, the percent reporting a credit relationship jumps from 24 percent in the
short run to 36 percent in the intermediate run, and among the largest firms, the percent
rises from 40 percent to 58 percent. Row 6 adds credit cards and the data continue to
support the importance of credit cards, but they play a smaller role in the intermediate run
than in the short run.38
Rows 7 and 8 of table 10 add to the numerator used in rows 5 and 6 the number of
households owning and actively managing a small business in 2010 that applied for credit as
a household. While such credit may not have been used for business purposes, we include
this calibration because the often close and complex interdependencies between household
and small business finance suggest “credit independence” is not necessarily the case. Put
differently, the calculations shown in rows 7 and 8 give the broadest possible indication of
the importance of credit to small businesses that the SCF can provide. These data show
that credit access is important to the vast majority of small businesses and the households
that own and manage them. All of the cells in rows 7 and 8 of both panels are over, and
typically well over, 75 percent.
38
This suggests that credit cards are perhaps used more for transactions purposes than as a permanent source of credit.
47
Primary financial institution
Table 11 shows the percentages of small businesses that identified various types of
financial institutions as their “primary financial institution” in 2010. As with previous similar
tables, established versus new firms are shown in the top panel, no-employee versus some
employees firms are given in the bottom panel. Overwhelming majorities of both
established and new small businesses and businesses across all size classes identified a
commercial bank as their primary financial institution. At the low end of the spectrum, 70
percent of the smallest new businesses said a commercial bank was their primary financial
institution. At the top end, 84 percent of the largest new firms did so. When we add the
percentages for savings banks and credit unions to the commercial bank percentages, the
percentages jump to between 85 percent and 94 percent. Having said this, it is noteworthy
that almost 9 percent of both established and new small businesses and almost 14 percent
of the no-employee businesses did not identify any financial institution type as their
primary financial institution (the last row in each panel). Around 5 percent of the other size
classes responded similarly. Still, it is clear that insured depositories, and especially
commercial banks, are by far the most important financial institutions for the vast majority
of small businesses.
Key financial services
Table 12A identifies the most important financial services used by a small business at its
primary financial institution (almost always a commercial bank), and table 12B examines
whether small businesses tend to bundle, or cluster, their use of financial services at these
48
institutions. Looking at table 12A, it is clear that the vast majority (over 75 percent) of both
established and new firms use a business checking account at their primary institution. In
addition, the incidence of use increases with business size, with 73 percent of even the
smallest firms saying they use a business checking account. Indeed, use of each service
increases with firm size. Business savings accounts are cited much less often than checking
accounts, but still relatively frequently by all of the six groupings of small businesses. On
balance, it is clear that the supply of deposit services is a central function of financial
intermediaries for small businesses.
The data in table 12A reinforce the importance of credit services to small businesses.
For both established and new firms, business lines of credit and (as in table 10) business
credit cards appear to be the most important credit services. This impression is reinforced
when the data are arrayed by firm size. For example, 32 percent of the largest firms and 7
percent of the smallest firms report having a business line of credit. Business credit cards
are used by 30 percent of the largest small businesses and 13 percent of the smallest firms.
In contrast, business mortgages are used by very few new and no-employee firms, although
their use increases to almost 6 percent by established firms and to almost 8 percent by the
largest small businesses.39
In addition to deposit and credit services, the data in table 12A indicate that business
payroll services, a type of payments service, are important to some small businesses, and
more important to new than to established firms. In addition, 15 percent of the smallest
39
A business mortgage is any mortgage owed by the small business.
49
firms report using business payroll services, a proportion that rises only to 18 percent at the
largest small businesses.
The data in table 12B are somewhat ambiguous regarding whether small businesses
tend to cluster their use of financial services at their primary financial institution. In this
sense, these data are not a strong reinforcement of the importance of relationship finance.
For example, only 23 percent of established firms and 30 percent of new businesses say
they use more than two services at their primary institution, while 42 percent of established
and 34 percent of new firms say they use only one service. When firms are arrayed by size,
the data remain ambiguous. Thus, the extent of clustering of financial services and its
importance for relationship finance and antitrust analysis warrant future research.40
Local banking offices
Table 13 addresses the final small business finance issue to which this paper
contributes: the importance of local banking offices to small businesses. The table gives
the mean, median and 25th and 90th percentiles of the distribution of miles between a small
business and the nearest office of its primary financial institution in 2010, once again
separating the firms into six groups.41 It is clear that, according to this metric, local banking
offices remain highly important to both established and new small businesses and across all
size classes of firms. For example, the median distance across all the groupings of small
40
Bank antitrust analysis at the U.S. Department of Justice and the banking agencies assumes small businesses consume a cluster of services at their geographically local depository institution. 41
We cannot say if the primary financial institution is a small, medium or large firm. However, as already discussed, we can say the primary financial institution is almost always a commercial bank.
50
businesses is never more than three miles, and the mean distance ranges from a low of five
miles (for established and the two smallest size classes) to a maximum of six miles (for new
and the largest small businesses). Even the 90th percentile distances only range from 12 to
15 miles.
VI. Summary
This section summarizes our most important findings within the context of a unified
narrative of the experiences of small businesses owned and actively managed by
households over the financial crisis and the Great Recession. It also suggests key areas
needing further research and recommends additions and revisions to existing data.
The SCFs are a rich source of small business data
The SCFs are rich and underused sources of information on small businesses and the
households that own and actively manage them. Indeed, SCF data are unique in several
important ways, including
(1) Having three surveys conducted between 2007 and 2010, one of which is a panel,
(2) Containing extensive data on the close association between small business and
household finance,
(3) Including households that do not own a small business, and
(4) Collecting a substantial amount of information on small businesses’ use of financial
institutions and services.42 While it is difficult to benchmark precisely the SCF data with U.S.
Census data on small businesses, it is clear that the small businesses in the SCF represent a
42
Of course, the 2013 and subsequent SCFs will also have many of these advantages.
51
broad cross-section of firms on size, age, industrial classification, ownership structure, and
method of acquisition by the household. SCF and Census data appear to coincide well for
small businesses with at least one employee, but diverge for firms with no employees.
The financial crisis and Great Recession hurt small businesses
Our examination of the SCF data over the period just before, during and just after the
financial crisis and the ensuing Great Recession revealed a complex picture of small
businesses and their owner-managers. The financial crisis and the Great Recession severely
affected the vast majority of both established and new small business. For example,
between 2007 and 2010, median real revenues and profits fell by 33 percent and 51
percent, respectively.
The 2009 and 2010 SCFs indicate that while during the crisis and the Great Recession,
credit supply conditions were a concern for both established and new small businesses,
constraints were much more severe at new firms. For example, in 2009, 60 percent of new
firms said their credit needs were unfulfilled while 36 percent of established firms said this
was the case. In addition, credit conditions were tighter for the smallest firms. Credit
supply improved by 2010 for both established and new firms and for both small businesses
with no employees and those with at least one employee.
Households gave a variety of reasons for their small business either being denied credit
or expecting it to be denied credit. In general, reasons primarily internal to the firm were
the most common factors cited for their actual or expected denial of credit. For example, in
52
both 2009 and 2010, some type of credit history issue was typically the dominant reason
given for either the denial or expected denial of credit. Reasons associated with the type or
size of the business or its poor balance sheet were also cited frequently. Mostly external
factors, such as a weak economy or government regulation, were given much less often.
Small business and household finance are intimately connected
The interdependencies and other interactions between the finances of small businesses
and their owner-manager households are numerous and complex and continue to be an
important area for research. The vast majority of small businesses in the SCFs, typically
over 75 percent, were started by the household, and relatively few were inherited. In
addition, substantial percentages of households made or guaranteed a loan to their small
business. These percentages were stable across 2007 and 2010 and were more important
for new small businesses than for established firms. On average, about 20 percent of new
small businesses and 13 percent of established businesses had such a loan or guarantee.
Established firms had a higher probability of survival over 2007–2009 if they had such a loan
or guarantee. In addition, households that had access to credit were more likely to start a
small business over this period.
Multivariate statistical tests show the importance of distinguishing between established
and new firms when trying to identify the characteristics of households that have a small
business. For example, in both 2007 and 2010, a household was more likely to have an
established small business if it had
(1) Higher net worth,
(2) Less of its net worth in home equity,
53
(3) A partner, and
(4) Access to credit.
In contrast, while factors (1), (3) and (4) were also significantly correlated in both years
with the probability a household had a new firm, in both years a household was also more
likely to have a new small business if it were
(5) Younger,
(6) More educated, and
(7) Less risk averse.
While all of these results are interesting, many reinforce the existing literature (e.g., the
importance of household net worth and access to credit, and personal characteristics such
as age, education and risk attitude), and some deserve further investigation (e.g., the
consistently negative or zero correlation of household income with the probability of having
a small business and the positive correlation of being partnered), the home equity
correlations are especially intriguing and are uniquely well-suited to being examined using
the SCF. Our research is consistent with the simultaneous existence of two interpretations
of the data. First, conditional on having housing assets, we find that tapping into home
equity via any type of mortgage is positively associated with having a small business. This
result is consistent with the conventional wisdom that many small business owners use
their home as collateral for loans that support their business. Second, conditional on the
amount of the mortgages supported by a home, we find that holding a larger proportion of
total net worth in housing is a negative signal of small business ownership. This suggests
54
the new conclusion that non-housing forms of net worth may, at the margin, be more
valuable to the small business owner, perhaps because they are more liquid.
Laid-off workers responded in part by starting small businesses
Our results indicate that workers who lost their jobs during the Great Recession often
started their own small business. Multivariate tests using the 2007–2009 panel SCFs show
that households were more likely to start a small business during the heart of the crisis and
the Great Recession if in 2007 they had (1) higher income, (2) access to credit, (3) more
education, and (4) the head-of-household had been unemployed sometime in the year
before the 2009 re-interview. In addition, the relatively high rate of job loss by certain
segments of white-collar workers during the Great Recession is consistent with SCF data
that indicate a trend between 2007 and 2010 toward the creation of small businesses in the
“Professional Services” industrial classification.
In contrast to our analysis of new small businesses formation using backward-looking
cross-section data, the forward-looking estimations using the 2009 panel re-interview of
2007 SCF respondents find no correlation of household small business creation with the
household’s net worth, ratio of home equity to net worth, risk preferences, partnership
status, and age. However, both panel and cross-section results identify a positive
correlation for education and unemployment status in the 12 months before 2009. While
the reasons for the asymmetries between the panel and the cross-section results are not
always clear, we believe our analyses of both strongly indicate that both types of
information are highly valuable for researching the topics addressed in this paper and many
other issues in household and small business economics.
55
Small business survival factors are poorly understood
Once again exploiting data from the 2007 SCF and its 2009 panel re-interview, we
examined what variables 2007 values correlate with the probability that the business would
survive from 2007 through the re-interview. While a variety of variables are sometimes
correlated with the probability of survival, our primary conclusion is that the factors
correlated with the probability of small business survival are at best poorly understood, at
least for the recent crisis and Great Recession. As has been found in previous studies,
higher household net worth was generally (but not in all models) correlated with increased
chances of survival. This is an area in need of additional research.
Relationship finance remains important for small businesses
This study reinforces the importance of the idea of relationship finance to both
established and new small businesses. Indeed, access to credit is consistently significant in
our multivariate tests. The SCF data for 2010 indicate that these relationships are heavily
focused on commercial banks. Small businesses use deposit, credit and sometimes
payments services at their primary financial institution, institutions that are almost always a
commercial bank. Business checking and savings accounts are the most important deposit
services.
With respect to credit services, our research indicates that business lines of credit,
business loans and possibly bank credit cards are the most important credit services. In
contrast, business mortgages are used by a small share of established small businesses and
by even smaller proportions of new businesses. While access to credit is important even for
some of the smallest (no-employee) firms, the incidence of credit relationships increases
56
substantially with business size. In addition, credit relationships tend to increase in
importance over time for both established and new firms and for both smaller and larger
businesses.
Local banking offices remain highly important to both established and new small
businesses and to small businesses of all sizes. For example, the median distance between
a small business and the nearest office of its primary financial institution in 2010 across all
of the groupings of small businesses used in this paper is never more than three miles, and
other moments of this distribution are consistent with our conclusion. Thus, the continued
use of local markets for small business financial services in bank antitrust analysis is
supported. However, our results suggest that continuing to assume that small businesses
cluster their use of financial services at their primary financial institution is more
problematic.
Recommendations for future research and improved data
Throughout this paper, we have identified topics that we believe are especially in need
of further research. Key topics include deeper understanding of the
(1) Interdependencies and interconnections between household and small business
finance, including the roles of home equity and household income,
(2) Factors that affect the creation of small businesses, including the roles of
employment history and education,
(3) Factors that affect small business survival, and
(4) Factors that affect small business credit availability and their choices of financial
institutions and services.
57
In addition, we have not investigated a number of topics that could be studied with the
SCF, such as the roles of gender and race in small business finance, creation and survival.
All of these topics, and others, require high-quality data. With respect to the future
conduct of the SCF, we make three recommendations.
First, we recommend conducting future panel re-interviews. This research was aided
greatly by the availability of the 2009 panel re-interview of the 2007 SCF. This was the first
panel re-interview of SCF respondents since the major redesign of the survey in 1989, and
was done primarily in response to the financial crisis. The costs of such efforts could be
made manageable by not conducting the re-interview with the same frequency as the tri-
annual cross-section SCF, but often enough to provide data over the full economic cycle, or
by alternating cross-section and panel re-interviews.
Second, existing questions could be clarified, and perhaps augmented, to focus on how
households get financing for creating their small business, how this financing evolves over
time, and what types of collateral are used. As a corollary, it would be highly desirable to
get a clearer picture of the criteria small businesses use to choose their primary financial
institution and when in the life cycle of their business they make and may revise that
decision. We understand the difficulties of adding more questions to an already long
survey, but we believe we could get significant benefits from the combination of a small
number of additional questions, some culling of less useful inquiries, and some clarification
of existing questions.
Last, we recommend expansion of the sample size to as large a sample as budget
realities will allow. The SCF is a unique data source for many topics of intense interest to
58
policymakers, researchers, industry participants, and the general public that go far beyond
small business finance. However, analysis of many of these topics is often limited by the
small number of observations that occur as the researcher drills down in the data to gain a
deeper understanding of the subject under investigation.
59
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63
Table 1A: Probability of Owning and Actively Managing a Small Business (SB)
Marginal effects of logistic regressions of a 2007 and 2010 SB ownership dummy on contemporaneous HH variables. Variables may
be endogenous to SB ownership and should be interpreted as conditional correlations only. Standard errors in parenthesis.
Significance of p<0.01 designated by ***, p<0.05 is **, p<0.10 is *.
Variable
(1) (2) (1) (2) (1) (2) (1) (2)
log(HH Income) -0.093 -0.068 -0.136*** -0.146*** -0.086 -0.192** -0.064 -0.055
(0.064) (0.072) (0.037) (0.037) (0.105) (0.079) (0.100) (0.114)
HH Age -0.024*** -0.025*** -0.004 -0.003 -0.034*** -0.033*** -0.032*** -0.038***
(0.005) (0.006) (0.005) (0.005) (0.007) (0.009) (0.006) (0.008)
HH Educ -0.061** -0.064** -0.015 0.001 0.157*** 0.125** 0.144*** 0.101**
(0.029) (0.032) (0.026) (0.027) (0.049) (0.055) (0.042) (0.049)
log(HH Networth) 0.698*** 1.033*** 0.437*** 0.469*** 0.112* 0.079 0.084** 0.097
(0.068) (0.112) (0.068) (0.145) (0.064) (0.106) (0.038) (0.061)
Home to Networth -1.153*** -1.157*** -0.328 -0.322
(0.111) (0.196) (0.310) (0.275)
log(House) -0.448*** 0.154 0.151 0.049
(0.127) (0.162) (0.187) (0.173)
log(Mortgage) 0.059*** 0.044*** 0.094** 0.027
(0.019) (0.016) (0.047) (0.031)
HH Partnerned 0.393** 0.614*** 0.664*** 0.725*** 1.054*** 0.766** 0.791*** 0.574**
(0.159) (0.197) (0.131) (0.152) (0.271) (0.300) (0.220) (0.272)
HH Cred Access 0.264** 0.105 0.685*** 0.429*** 0.637*** 0.131 0.263 0.046
(0.135) (0.176) (0.112) (0.151) (0.235) (0.299) (0.192) (0.299)
RiskPref -0.117 -0.159 -0.106 -0.080 -0.268** -0.279** -0.404*** -0.380***
(0.094) (0.104) (0.071) (0.079) (0.114) (0.129) (0.095) (0.122)
Pseudo-Rsq 0.28 0.25 0.24 0.20 0.15 0.14 0.12 0.11
N 4,073 4,073 6,106 6,106 3,334 3,334 5,261 5,261
Established versus No Small Business New versus No Small Business
2007 2010 2007 2010
64
Table 1B: Probability of Owning and Actively Managing a Small Business (SB)
Marginal effects of logistic regressions of a 2007 and 2010 SB ownership dummy on contemporaneous HH variables. Variables may
be endogenous to SB ownership and should be interpreted as conditional correlations only. Standard errors in parenthesis.
Significance of p<0.01 designated by ***, p<0.05 is **, p<0.10 is *.
Variable
(1) (2) (1) (2) (1) (2) (1) (2)
log(HH Income) 0.041 -0.076 -0.163*** -0.180*** -0.106 -0.094 -0.105** -0.115***
(0.125) (0.089) (0.048) (0.049) (0.068) (0.068) (0.041) (0.040)
HH Age -0.012* -0.013* -0.004 -0.005 -0.035*** -0.040*** -0.013** -0.015**
(0.006) (0.007) (0.004) (0.005) (0.006) (0.007) (0.006) (0.006)
HH Educ 0.115*** 0.102** 0.064** 0.035 -0.074** -0.100*** 0.013 0.015
(0.040) (0.042) (0.032) (0.037) (0.037) (0.035) (0.031) (0.031)
log(HH Networth) 0.152** 0.037 0.136*** 0.122** 0.728*** 1.300*** 0.435*** 0.572***
(0.066) (0.075) (0.040) (0.056) (0.106) (0.109) (0.081) (0.172)
Home to Networth -0.134 -0.608*** -1.473*** -1.107***
(0.266) (0.218) (0.297) (0.237)
log(House) 0.297** 0.238* -0.674*** 0.037
(0.140) (0.129) (0.125) (0.183)
log(Mortgage) 0.009 0.052** 0.092*** 0.036*
(0.025) (0.022) (0.022) (0.019)
HH Partnerned 0.480** 0.562** 0.554*** 0.513*** 0.697*** 0.743*** 0.882*** 0.845***
(0.207) (0.237) (0.158) (0.185) (0.184) (0.223) (0.156) (0.183)
HH Cred Access 0.059 -0.042 0.748*** 0.426** 0.615*** 0.244 0.445*** 0.268
(0.164) (0.218) (0.154) (0.210) (0.163) (0.214) (0.125) (0.175)
RiskPref -0.221* -0.311** -0.210** -0.189* -0.123 -0.104 -0.215*** -0.137
(0.114) (0.126) (0.084) (0.098) (0.091) (0.104) (0.077) (0.089)
Pseudo-Rsq 0.09 0.09 0.10 0.09 0.31 0.30 0.24 0.21
N 3,402 3,402 5,389 5,389 4,005 4,005 5,979 5,979
Employee versus No Small BusinessNo Employee versus No Small Business
2007 20102007 2010
65
Table 2A: Key Characteristics of Primary SBs Actively Managed by HHs
2007 2010
Mean Median P25 P90 Mean Median P25 P90
Established SB
# Employees 8.3 1 0 14 8.6 1 0 14
Bus Income 523 41 13 500 417 20 2 300
Bus Sales 2,029 119 31 2,000 6,294 80 23 2,100
Bus Value 2,841 110 15 3,846 2,267 72 10 2,433
HH Bus Loan 0.131 0.129
Amt (% of Sales) 1.28† 0.145 0.03 2.24 6.38 0.227 0.067 5.4
New SB
# Employees 5.3† 1 0 9 1.5
* 1 0 4
Bus Income 124* 3.5 0 100 41* 0.5 0 65
Bus Sales 968 15 1 406 217 7 0 200
Bus Value 623* 22 2 699 659* 21 1 391
HH Bus Loan 0.217* 0.193*
Amt (% of Sales) 2.10*† 0.6 0.210 5.21 172.7* 0.5 0.30 4.5
†Mean significantly different from 2010 at 5% or greater.
*Mean significantly different from HHs with Established SB at 5 % or greater.
66
Table 2B: Key Characteristics of Primary SBs Actively Managed by HHs
2007 2010
Mean Median P25 P90 Mean Median P25 P90
No Emp SB
Age 10.2 7 2 24 10.6 9 3 28
Bus Income 244 11 2 90 269 10 0 70
Bus Sales 497 119 31 2,000 8,124 24 3 160
Bus Value 162 8 0 250 124 19 3 190
HH Bus Loan 0.119† 0.053
Amt (% of Sales) 3.64† 0.586 0.076 4.81 20.8 0.481 0.143 18.8
Emp SB
Age 11.9*† 9 3 28 13.4 10 4 31
Bus Income 505 52 9 700 356 17 0 400
Bus Sales 2,523* 167 45 2,880 2,449 112 24 266
Bus Value 949*† 122 34 2,000 730* 95 19 1,283
HH Bus Loan 0.185* 0.209*
Amt (% of Sales) 0.822*† 0.250 0.033 2.00 44.2 0.302 0.077 4
†Mean significantly different from 2010 at 5% or greater.
*Mean significantly different from HHs with no employee SB at 5 % or greater.
67
Table 3: Industry Classifications
%Primary SBs Actively Managed by HHs
2007 2010
Established New Established New
Agricultural 7.7 2.2 7.0 5.8
Mining 21.5 11.6 14.2 11.5
Manufacturing 7.1 6.4 5.8 8.9
Wholesale/Retail 11.6 20.8 15.3 14.3
Lower Tech Service
13.5 19.8 17.2 11.8
Prof Services 38.7 39.2 40.6 47.7
2007 2010
No Employee
Employee=1,2 Employee>=3 No Employee
Employee=1,2 Employee>=3
Agricultural 7.0 4.1 6.2 7.7 7.5 8.4
Mining 16.5 21.9 18.1 13.7 14.2 9.0
Manufacturing 6.7 5.9 7.8 7.0 6.0 4.9
Wholesale/Retail 13.8 16.1 14.0 14.1 14.0 14.0
Lower Tech Service
14.1 16.5 16.2 13.3 19.2 12.8
Prof Services 42.0 35.5 37.69 44.2 39.1 50.8
68
Table 4: SB Ownership Structure
% of Primary SBs Actively Managed by a HH
2007 2010
Established New Established New
Sole Proprietor 46.7 50.1 49.7 50.0
Subchapter S 17.4 12.2 17.0 5.2
LLC/LLP 13.8 23.9 19.4 14.9
Partnership 12.7 8.6 6.5 29.9
Other 9.4 5.2 7.5 4.0
2007 2010
No Employee
Employee=1,2
Employee>=3 No Employee
Employee=1,2
Employee>=3
Sole Proprietor 68.6 46.9 23.2 72.9 46.5 19.1
Subchapter S 11.2 13.0 23.7 5.6 11.5 29.4
LLC/LLP 14.9 16.1 19.8 16.1 26.5 25.8
Partnership 2.5 17.9 17.4 1.8 11.5 10.7
Other 2.9 4.5 15.9 3.2 3.6 15.0
69
Table 5: Methods of Acquiring a SB
% of Primary SBs Actively Managed by a HH
2007 2010
Established New Established New
Bought/Invest 14.5 17.5 14.6 11.1
Started 73.7 75.8 76.3 81.8
Inherited 5.9 2.5 4.0 3.2
Join/Promote 5.9 4.2 5.2 3.9
2007 2010
No Employee
Employee=1,2 Employee>=3 No Employee
Employee=1,2 Employee>=3
Bought/Invest 6.3 15.0 26.9 8.8 11.2 23.8
Started 86.9 80.0 54.7 86.8 80.6 60.6
Inherited 4.7 0.8 8.2 2.6 4.2 4.9
Join/Promote 2.1 4.3 10.2 1.7 3.9 10.5
70
Table 6: Probability of Survival 2007–2009
Logistic regressions of a 2007-2009 survival dummy on 2007 household and business variables. Industry dummies are deemed
significant if at least one of the industry dummies is significantly different at the indicated level from the professional services
industry (excluded dummy). Survival is defined as the continued ownership of the business by the household. Failed is defined as a
termination of the business itself: “Went out of business” to P09502 in the 2009 SCF. Marginal effects reported.
Standard errors in parenthesis. Significance of p<0.01 designated by ***, p<0.05 is **, p<0.10 is *.
Variable Pooled Established New No Employee Employee
log(HHIncome) -0.085 0.027 -0.870** -0.321** 0.030(0.072) (0.072) (0.401) (0.130) (0.081)
log(HHNetworth) 0.106** 0.064 0.320*** 0.109 0.173***(0.044) (0.062) (0.104) (0.071) (0.064)
Homeowner -0.008 0.382 0.553 0.104 0.794(0.442) (0.831) (1.086) (0.715) (1.421)
Age1 -0.225 14.973*** -1.167 -0.073 0.445(0.442) (0.835) (0.896) (0.699) (0.859)
Age2 0.513 0.994** 0.693 1.068** 0.161(0.337) (0.450) (0.713) (0.461) (0.577)
Educ -0.031 -0.108 0.157 0.076 -0.059(0.083) (0.112) (0.150) (0.105) (0.141)
HHPartner 0.720* 0.445 1.754** 0.583 0.401(0.434) (0.593) (0.870) (0.613) (0.977)
HHCredAccess -0.235 -0.388 0.486 -0.164 0.112(0.595) (0.638) (1.470) (0.672) (1.041)
RiskPref -0.210 0.093 -0.744 0.026 -0.778*(0.257) (0.354) (0.634) (0.324) (0.422)
log(BusIncome) 0.039 0.104* -0.009 0.085 -0.019(0.036) (0.062) (0.071) (0.052) (0.070)
log(BusValue) 0.052 0.102* -0.019 0.053 0.094(0.035) (0.058) (0.061) (0.046) (0.089)
Emp 0.944** 0.960 1.215(0.451) (0.795) (0.913)
HHBusLoan 0.314 1.296* 0.178 0.817 0.185(0.396) (0.782) (0.807) (0.543) (0.593)
Corp 0.306 0.730 -0.024 0.188 0.311(0.348) (0.632) (0.648) (0.522) (0.536)
BusAge5 -0.351 0.146 -1.328**(0.372) (0.468) (0.545)
Industry Dummies Y Y Y*** Y Y*
Pseudo R-sq 0.26 0.28 0.54 0.36 0.27
N 1,018 835 183 239 777
71
Table 7: Probability of Starting New Small Business During Crisis (Given No SB in 2007)
The first column reports the results of a logistic regression of a 2007-2009 SB creation dummy on 2007 household characteristics and
a 2009 dummy for unemployment for the HH in the previous 12 months from the time of the 2009 interview. Sample is limited to
only those HHs that did not own a SB in 2007. The second column reports the results of a logistic regression of 2009 ownership of a
new SB on contemporaneous 2009 household characteristics. In this case SB=1 if the household owns a SB two years or younger and
SB=0 if the household does not own a SB in 2009. Due to data limitations, HH Cred Access cannot be constructed in 2009. The third
column reports a regression comparable to the second column using 2007 variables. In percentage probability terms, not decimal.
Standard errors in parenthesis. Significance of p<0.01 designated by ***, p<0.05 is **, p<0.10 is *.
Variable Panel Cross Section 2009 Cross Section 2007
HH Educ 0.285** 0.475*** 0.229***
(0.128) (0.158) (0.089)
log(HH Income) 0.946** 0.219 -0.039
(0.368) (0.434) (0.208)
Age -0.006 -0.046** -0.053***
(0.021) (0.020) (0.012)
RiskPref -0.558 -0.820** -0.333*
(0.348) (0.370) (0.194)
HH Partner 0.503 2.038*** 1.675***
(0.610) (0.768) (0.470)
log(Networth) -0.029 0.255** 0.268
(0.097) (0.120) (0.163)
Home to Networth 0.027 -1.649* -0.812
(0.928) (0.965) (0.588)
HH Cred Access 1.577*
(0.881)
Unemp 12 Mo 2007 -0.361 -0.223 0.578
(0.824) (0.992) (0.499)
Unemp 12 Mo 2009 2.241*** 1.679**
(0.627) (0.826)
Pseudo R-sq 0.10 0.11 0.14
N 2,712 2,919 3,284
72
Table 8: Access to Credit in 2009 and 2010
% of Primary SBs Actively Managed by a HH
2009 2010
Established New Established New
1) Applied for Credit 21.9 26.8 25.3 17.7
2) Applied, but Fully or Partially Denied
11.3 41.0 19.8 25.4
3) Wanted Credit but Did Not Apply b/c Expected Denial
8.3 12.8 7.9 6.9
4) Wanted Credit 30.2 39.6 33.2 24.6
5) Credit Needs Unfulfilled
35.7 60.0 38.9 46.3
2009 2010
No Employee
Employee=1,2
Employee>=3
No Employee
Employee=1,2
Employee>=3
1) Applied for Credit 9.9 21.9 38.4 10.5 21.5 44.7
2) Applied, but Fully or Partially Denied
27.5 17.3 20.8 19.7 25.0 19.0
3) Wanted Credit but Did Not Apply b/c Expected Denial
7.6 6.8 13.8 4.9 8.9 10.2
4) Wanted Credit 17.5 28.7 52.2 15.4 30.4 54.9
5) Credit Needs Unfulfilled
59.0 36.9 41.7 45.3 47.0 34.0
73
Table 9: Reasons Credit Either Was or Was Expected to Be Denied in 2010
% of Responses
2009 2010
Denied Expected Denied (given not denied)
Denied Expected Denied (given not denied)
Poor Balance Sheet 9.8 26.6 36.0 25.3
Credit History 28.1 39.4 38.9 32.1
Type/Size of Business 21.3 7.9 14.2 10.0
Viability of Business 33.7 5.6 10.8 16.4
Informational Problems 0.0 0.0 0.0 4.5
Weak Economy 4.5 19.8 0.0 10.4
Govt Regulation 2.0 0.6 0.0 0.6
Other 0.6 0.1 0.0 0.8
74
Table 10: Credit Relationships of SBs 2010
% of Primary SBs Actively Managed by a HH
Established New
(1) Bank Credit (ex. CC) 19.8 16.8
(2) Bank Credit (in CC) 38.6 34.6
(3) Credit Relation (ex CC) 23.8 24.0
(4) Credit Relation (in CC) 39.8 37.2
(5) Cred Rel or Bus Appl for Cred in Prev 5 yrs (ex CC)
36.0 32.9
(6) Cred Rel or Bus Appl for Cred in Prev 5 yrs (in CC)
47.6 43.2
(7) Cred Rel or Bus/HH Appl for Cred in Prev 5 yrs (ex CC)
79.9 86.2
(8) Cred Rel or Bus/HH Appl for Cred in Prev 5 yrs (in CC)
83.6 88.1
No Employee Employee=1,2 Employee>=3
(1) Bank Credit (ex. CC) 8.8 16.7 35.1
(2) Bank Credit (in CC) 27.0 36.0 55.1
(3) Credit Relation (ex CC) 12.2 24.5 40.3
(4) Credit Relation (in CC) 27.4 38.4 57.5
(5) Cred Rel or Bus Appl for Cred in Prev 5 yrs (ex CC)
18.9 36.6 57.9
(6) Cred Rel or Bus Appl for Cred in Prev 5 yrs (in CC)
31.2 46.9 67.1
(7) Cred Rel or Bus/HH Appl for Cred in Prev 5 yrs (ex CC)
78.5 80.6 87.0
(8) Cred Rel or Bus/HH Appl for Cred in Prev 5 yrs (in CC)
82.4 83.4 89.6
(1) Reflects the presence of a personal bank loan used for business purposes, business bank loan, or business line of credit in
questions regarding services used at the primary financial institution or sources of external finance for the ongoing
operation or expansion of the SB in the previous year.
(2) Reflects (1) plus the use of a credit card.
(3) Reflects (1) plus the use of an “other” credit relationship for external financing of the SB.
(4) Reflects (3) plus credit cards.
(5) Reflects (3) plus any SBs that applied for credit in the lesser of the previous 5 years or since existence.
(6) Reflects (5) plus credit cards.
(7) Reflects (5) plus any SBs where the owner/manager HH applied for credit in the lesser of the previous 5 years or since
existence.
(8) Reflects (7) plus credit cards.
75
Table 11: Primary Financial Institution of a SB in 2010
% of Primary SBs Actively Managed by a HH
Established New
Commercial Bank 76.7 76.5
Savings Bank 7.5 5.2
Credit Union 5.4 8.9
Fin/Loan Co 0.8 0.5
Brokerage 0.2 0.2
Mortgage Co 0.3 0.0
Other 0.0 0.0
None 8.7 8.8
No Employee Employee=1,2 Employee>=3
Commercial Bank 70.4 78.3 84.1
Savings Bank 3.1 7.0 6.8
Credit Union 11.3 7.5 2.7
Fin/Loan Co 1.0 0.4 0.7
Brokerage 0.4 0.1 0.0
Mortgage Co 0.1 0.7 0.0
Other 0.0 0.0 0.0
None 13.6 6.1 4.5
76
Table 12A: Financial Services Used By a SB at its Primary Financial Institution in 2010
% of Primary SBs Actively Managed by a HH
Established New
Business Checking 83.4 76.2
Business Savings 24.4 26.0
Business Line of Credit
16.0 13.9
Business Mortgage 5.7 1.8
Business Credit Card
19.2 16.3
Business Payroll 14.5 22.3
None 3.7 6.9
No Employee Employee=1,2 Employee>=3
Business Checking 73.2 84.3 90.9
Business Savings 17.9 22.5 37.6
Business Line of Credit
7.1 12.3 31.7
Business Mortgage 1.8 5.9 7.7
Business Credit Card
12.6 16.4 29.7
Business Payroll 14.6 17.2 18.3
None 7.2 3.2 1.9
77
Table 12B: Number of Financial Services Used by a SB at its Primary Financial Institution in 2010 (Given at
least 1)
Established New
1 Service 41.7 34.4
2 Services 35.3 35.3
>2 Services 23.0 30.4
At least 1 Credit and 1 Deposit Service
33.7 38.5
No Employee
Employee=1,2 Employee>=3
1 Service 47.3 40.0 37.9
2 Services 33.1 35.9 33.1
>2 Services 19.1 24.1 29.0
At least 1 Credit and 1 Deposit Service
30.4 59.8 41.0
Table 13: Distance (miles) to Primary Financial Institution from SB in 2010
2010
Mean Median P25 P90
Established 5.04 2 0 12
New 6.13 3 0 15
0 Employees 5.06 2 0 12
1,2 Employees 5.08 2 0 11
≥ 3 Employees 5.91 2 0 15
Differences not significant at p<0.10.
78
Appendix A: Variable Definitions
HH Income Total Household (HH) Income
Non Bus HH Income Total HH Income less income from Small Business (SB) and rental income
HH Age Age in years
HH Educ Years of Education
Non Bus Networth HH Net Worth less value of SB
Networth Total HH Networth (including SB)
Homeowner Dummy = 1 if HH owns a home
HH Partnered Dummy = 1 if HH is married or has a partner
HH Cred Access Dummy = 1 if HH has a loan from an Insured Depository Institutions (other than credit cards (CC) or educational loans)
Home to Networth Fraction of Networth held in home equity (bounded to [0,1])
Risk Prefs Subjective self-assessment of riskiness 1–4 (larger is more risk averse)
# Employees Number of employees at SB, including self
Bus Income Profits of SB
Bus Sales Sales of SB
Bus Value Total value of SB (irrespective of HH's share)
Bus Age Calendar year age of business
HH Bus Loan Dummy = 1 if the HH made loan to SB
Amt (% of Sales) Given HHBusLoan=1, the fraction of Loan to Bus Sales
Age1 Dummy = 1 if HH Age<35
Age2 Dummy = 1 if 35<=HH Age<62
Emp Dummy = 1 if # Employees>=3
Corp Dummy = 1 if SB is an Limited Liability Corporation or S Corp
BusAge5 Dummy = 1 if Business <= 5 years old
Unemp 12 Mo 2007 Dummy = 1 if head of HH was unemployed at any time in 12 months before 2007 SCF
Unemp 12 Mo 2009 Dummy = 1 if head of HH was unemployed at any time in 12 months before 2009 SCF
SB Dummy = 1 if HH owns and actively manages a SB and their primary business is a non-farm with less than 500 employees. Dummy = 0 if HH is a non-farm HH that does not have a SB with greater than 500 employees
NEW SB less than or equal to 3 calendar years old
ESTABLISHED SB greater than 3 calendar years old
Survival Dummy = 1 if SB=0 in 2007 and the SB went out of business, =1 if business survived until 2009 or was sold
Started SB Dummy = 1 if SB=0 in 2007 and SB=1 in 2009
House Value of all residential houses owned by HH
Mortgage Value of all HH debt collateralized by residential housing
All dollar values expressed in thousands of 2007 dollars.
79
Appendix B: Univariate Analysis
This appendix provides univariate results that help to form the basis for the multivariate logit
regressions discussed in the text.
I. Households that own and actively manage a small business
Table B1.A and B1.B compare three sets of households with each other, between 2007 and 2010,
across four dimensions. The top panel of each table provides key characteristics of households that do not
own and actively manage a small business (non-SB owners) in each of the years. The middle panel of B1.A
shows the same variables for households that own and actively manage an established small business (Est.
SB), and the middle panel of table B1.B displays the same variables for households with a small business
that has no employees (No Emp SB). Similarly, the bottom panel of table B1.A provides comparable data
for households that own and actively manage a new small business (New SB), and the bottom panel of
table B1.B displays data for households with a small business that has at least one employee (Emp SB).43
The number of observations is provided in each cell. Because many distributions in the SCF (and other
household and small business surveys) are highly skewed, each continuous variable’s mean plus its median
(P50), 25th (P25) and 90th (P90) percentile values are shown.44 For small business owners, two measures of
income and net worth are provided: one includes business income or the value of the business, as
appropriate, and the other does not.45 In addition, for all three groups of households, the ratio of home
equity to total net worth (including small business equity where applicable) is given. To our knowledge,
43
To preserve consistency with the groups of households that own a small business, the non-SB owners grouping of households also excludes farmers as defined in the main text. 44
See, for example, Cole and Sokolyk (2013), Bricker et al. (2012) and Mach and Wolken (2006). 45
In this and all tables here and in the text dollar values are given in 2007 dollars. Our inflation deflator is the annual average of the all items Consumer Price Index Research Series Using Current Methods for urban households (CPI-U-RS), computed by the U.S. Bureau of Labor Statistics.
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this is the first study of small business owners to separate home equity from other components of net
worth, a potentially important analytical advantage, and a major benefit of using the SCF. Separate
compilations are provided for established, new, no-employee and employee small businesses, but the
concepts are not combined in order to maintain sufficient sample size. Last, when comparing variables
across the two years, it is important to remember that the samples are two separate cross-sections of
households, not a panel of the same households.
Households with an established or new small business
The univariate comparisons in table B1.A suggest several broad but preliminary conclusions. First,
households with established or new small businesses differ in several statistically and economically
significant ways from households that do not own a small business. Indeed, the means of all of the
variables listed in the top panel are significantly different at the households with established or new small
businesses from the means at the non-SB owners. On average, households with either type of small
business have statistically and substantially higher incomes, over a year more education, much higher net
worth by either measure shown, a higher percentage of home ownership, a lower percentage of their net
worth in housing if they own a home, are more likely to have a spouse or other personal partner, and
lower levels of professed risk aversion. All of these differences in means exist in both 2007 and 2010.
Moreover, where relevant, virtually all of these impressions hold up to comparisons of medians and the
two other percentiles shown. The average age of non-SB owners in both years is statistically (but only
slightly) less than that of the average owner of an established SB, but statistically higher by a little over
seven years than the mean age of the owner of a new small business. However, these age results do not
necessarily hold up across the three percentiles of the distribution. Last, while prior to the crisis (2007) the
percent of households with access to credit is about the same across all three groups, post crisis (2010) the
percent is significantly smaller at the non-SB households.
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Second, while the means of most of the variables differ in expected and significantly different ways
between households with established small businesses and those with new small businesses, there are
some interesting exceptions. In 2007, households owning an established business had statistically higher
mean incomes than households with a new small business, and this result holds in 2010 if business income
is included. However, when business income is excluded, in 2010 both sets of households have
comparable average incomes. Moreover, P25 and median values of this latter measure of income are
lower at established firms in both years. Clearly, households with established firms rely relatively more on
income from their firms than do households owning a new business. In addition, the mean net worth of
households with established firms is significantly larger than that of households with a new firm in both
years across both definitions of net worth, and this result holds across the other moments of the
distribution shown in table B1.A. The heads of households with established small businesses tended to be
older, to have slightly less education, and to be more likely to own a home than the heads of households
with new small businesses, but there is no difference in their probability of being partnered or their mean
risk preference. When a home was owned, in 2007 the percent of total net worth held in the home was
higher at households with a new small business, but this was not the case in 2010. Moreover, in both
years a similar pattern is observed in the medians of this ratio. Last, before the crisis, households with an
established small business were significantly less likely to have access to credit than were households with
a new small business, but this (statistical) difference disappears in 2010.
The third broad impression provided by the data in table B1.A is that the financial crisis and the
ensuing recession significantly and adversely affected both households that did not have a small business
and those with an established firm. Looking first at the non-SB owners, in 2010 such households had, on
average, significantly lower real income (down 8 percent) and less net worth (down 16 percent), and
tended to have slightly more education and to be a little more risk averse than the comparable cross-
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section in 2007. In addition, consistent with a steep decline in home values, the ratio of home equity to
total net worth fell, as did the percent of households with access to credit.
Turning to households owning an established firm and using the income and net worth measures that
include small businesses, we find that households with established small business also lost income (down
21 percent) and net worth (down 24 percent). Means of household income and net worth, excluding small
business income and equity, fell 17 percent and 19 percent, respectively. The group of such households
was slightly more risk averse in 2010 than in 2007, their average age increased and their mean years of
education remained unchanged. Neither households with an established nor those with a new business
experienced significant changes in either average homeownership or partnership rates. However, for both
groups, the average ratio of home equity to total net worth (including equity in the small business) fell,
and the percent of households reporting access to credit increased.
The fourth general impression from table B1.A is that, in contrast to the non-SB owners and the
established small business household groups, many of the mean characteristics of households that
successfully started a new small business in the three years before either 2007 or 2010 were little changed
between those years. This result is perhaps surprising, given the differences found for the other two
household groups and the obvious differences between the three years prior to 2007 versus 2010.
Statistical tests of the difference in means indicate that only the level of real net worth (excluding the
value of the small business) and the percentages of households that were homeowners or used bank
credit changed significantly. Interestingly, the non-small business components of net worth increased by 7
percent. Less surprisingly, the percent of households owning a home declined from 79 percent in 2007 to
70 percent in 2010 and the percent reporting access to credit also declined significantly. These patterns
are consistent with the view that while the financial endowment needed to start a small business rose
during the crisis period, perhaps because of increased credit constraints, the ability of housing net worth
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to provide that endowment declined, consistent with a precipitous decline in housing prices and the
overall decline in homeownership.
Comparisons of the inter-temporal patterns of the medians and other percentiles of the distributions
of households that started a new small business in the three years before either 2007 or 2010 suggest a
more complex and perhaps less surprising story than that provided by the means. While the head of
household’s age and years of education remain essentially constant within the group across the two
surveys, median and P25 values of both measures of household income and both measures of net worth
all declined substantially from 2007 to 2010, and only increased at the 90th percentile. Thus, as was the
case for the “non-SB owners” and the owners of established small businesses, it is clear that the owners of
new small businesses in the lower portions of these distributions were typically much worse off in 2010
than the comparable group in 2007.
Households whose small business had no or some employees
As was true in table B1.A, the univariate comparisons of table B1.B suggest several broad but
preliminary conclusions that in most cases need to be subjected to multivariate tests. First, both no-
employee and households whose small business has one or more employees differ in several statistically
and economically significant ways from households that do not own a small business. Indeed, once again
the means of all of the variables listed in the top panel are significantly different at the households with
either type of small business than the means at the non-SB owners. On average and in both 2007 and
2010, households with either type of small business had higher incomes, more education, and greater net
worth were more likely to own a home, had a lower ratio of home equity to total net worth if they owned
a home, were more likely to be partnered, and had lower levels of professed risk aversion. Where
relevant, virtually all of these impressions hold up to comparisons of medians and the two other
percentiles shown. In 2007, households with a small business were slightly younger than households that
did not own a small business, but this was not necessarily the case in 2010. In addition, in 2007 credit
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access did not present a uniform pattern across the household groups, but in 2010 households with a small
business consistently were more likely to have access to credit.
Second, in both years the households whose small business had no employees generally differed
significantly from households whose small business had at least one employee. On average in both years,
households whose small business had at least one employee had higher incomes using both measures of
income, were older, had higher net worth using both measures of net worth, were more likely to own a
home, had a lower ratio of home equity to total net worth if they owned a home, and were more likely to
be partnered. Interestingly, in 2007 the mean education level of the two groups was the same and
households whose small business had at least one employee were much more likely to have credit access.
However, by 2010 households with at least one employee had (slightly) more education but were no more
likely to have access to credit. In both years there is little or no difference in reported risk preference
between the two groups. All of these impressions are supported by the other moments of the
distributions shown.
Comparisons between 2007 and 2010 of the two groups of households that owned and actively
managed a small business indicate that the financial crisis and recession severely affected both groups,
especially households at or below the median level of a given variable. Thus, mean real total income of
households whose small business had at least one employee fell by 19 percent, and their real total net
worth declined by 11 percent. Households whose small business had no employees showed no statistically
significant decline in either average income measure, but both mean measures of real net worth fell
significantly and substantially. The average age of both groups of households increased, and both groups
reported slightly higher levels of risk aversion. Reported access to credit increased for households whose
small business had no employees, but remained statistically unchanged for households whose business
had at least one employee.
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II. Small business survival and failure
Table B2 compares key characteristics in 2007 of small business-owning households whose firms would
survive from 2007 to 2009 (top panel) with those of households whose firms would fail (bottom panel)
over that period. The data in this and the next section in this appendix use the panel of households
provided by the 2007 and 2009 SCFs.
In 2007, households with small businesses that would survive had higher levels of real income (both
including and excluding income from the small business) and real net worth (excluding the value of the
small business) than households whose firms would fail. Median household income (including income
from the small business) was 43 percent greater and median non-business net worth 209 percent larger at
the households whose firms survived. Households whose firms would survive were 18 percentage points
more likely to own a home, but the mean value of their ratio of home equity to total net worth was not
significantly different from that of households whose small business failed. Also, there were no
statistically significant differences in the means between the two sets of households with respect to the
heads of household’s age, years of education, partnership status, access to credit and degree of risk
preference.
Turning to the small businesses themselves, table B3 compares key characteristics in 2007 of small
businesses that would survive from 2007 to 2009 (top panel) with those of small businesses that would fail
(bottom panel). Mean values of all seven characteristics shown differ significantly across the two groups,
and these differences hold up across the three percentile points given. More specifically, in 2007 all four
measures of firm size—number of employees, business income, total sales, and business value—are
substantially larger at the businesses that would survive the next two years. In addition, the firms that
would survive were older—on average by about five years—than the firms that would fail. Small
businesses that would survive were slightly more likely to have a loan or financial guarantee from their
owner-manager household than were the small businesses that would fail. However, consistent with the
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data in tables 2A and 2B, well under 25 percent of firms in either group had such a financial relationship
with their owner-manager household. When such a loan or guarantee did exist, the combination of the
two was a much smaller percentage of sales in 2007 (on average about one-fifth as great) at the firms that
would survive.
Table B4 separates the surviving and failed firms as of 2007 according to the same industry
classifications used in table 3. While the percentages clearly differ between the two groups, only the
“wholesale/retail” and the “lower-tech services” classifications appear noteworthy. Both of these
categories are substantially smaller among the businesses that would survive. Indeed, only about 27
percent of the firms that would survive belong to one of these categories, as compared with almost 42
percent of the businesses that would fail.
Small businesses that would survive or fail are classified by their 2007 ownership structure in table B5.
The two structures that clearly stand out as differing between the two groups are “sole proprietor” and
“Subchapter S.” Forty-three percent of the firms that would survive over 2007–2009 were sole
proprietorships in 2007, but 63 percent of those that would fail had adopted this ownership form. In
contrast, almost 18 percent of the firms that would survive were Subchapter S corporations in 2007,
compared with not quite 5 percent of the firms that would fail.
As was true in section I of this appendix, the univariate comparisons in this section suggest several
broad but preliminary conclusions. In 2007, households with small businesses that would survive the next
two years generally had higher levels of income and non-business net worth and were more likely to own a
home than were households whose firms would fail. Firms that would survive were generally larger across
several measures of size and tended to be older. Firms that survived were slightly more likely to have a
loan or financial guarantee from their owner-manager household. When such a loan or guarantee existed,
it was usually a much smaller percentage of sales at firms that would survive. While industry classifications
generally did not appear to differ much between the two classes of firms, notable exceptions are the
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“wholesale/retail” and “lower-tech services” groups, both of which had substantially smaller percentages
of firms that survived. Last, sole proprietorships were greatly underrepresented and Subchapter S
corporations were substantially overrepresented in the group of small businesses that would survive.
III. Small business creation
Table B6 compares selected characteristics of households that started a small business in the 2007–09
period (top panel) with those who did not (bottom panel). It is apparent that, with only the two
exceptions of the ratio of home equity to net worth and the household head’s unemployment status in the
12 months prior to the 2007 survey, the means of all of the variables shown are statistically different
between households that started a new business and households that did not. Moreover, where relevant,
most of these differences are sustained across the other moments of the distributions shown. Thus, the
heads of households that started a small business during the crisis and the Great Recession tended in 2007
to have higher income and greater net worth, to be younger, to have more education, to be more likely to
be partnered, to be less risk averse, and to be more likely to have access to credit than the heads of
households who did not start a small business. Some of these characteristics (e.g., income, net worth and
education) would seem to describe white-collar workers more than other types of employees.
The last two variables in each panel of table B6 provide important details regarding the employment
history of the two household groups. The indicator variables for “Unemp 12 Mo 2007” and “Unemp 12 Mo
2009” give the percentage of heads of household who were unemployed at any time in the 12 months
before 2007 and 2009 surveys, respectively. Thus, in the pre-recession year of 2007, the same percentage
(11 percent) of heads of household had been unemployed sometime in the previous 12 months in both
household groups. However, by 2009, 29 percent of the household heads where a new business was
started had been unemployed in the previous year, but this was true at only 16 percent of the households
that did not start a business. Thus, these data support the conjecture that the sharp rise in unemployment
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during the Great Recession was an important driver in the creation of new small businesses during that
period.
IV. Comparing SCF and U.S. Census Data
Tables B7 and B8 compare the distribution of SCF data for firms with at least one employee with the
distribution for comparable firms as reported in the U.S. Bureau of the Census’ Statistics of U.S. Businesses
(SUSB), all in 2007. Table B7 separates firms into 20 industrial categories, and table B8 divides businesses
into five groups based on the number of employees. As reported in Section II of the main text, it is clear
that the SCF and Census distributions are similar.
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Table B1.A: Characteristics of HHs That Own and Actively Managed Small Businesses and Those That Do Not
2007 2010 Mean P50 P25 P90 Mean P50 P25 P90
Non SB Owners N=3135 N=5031
HH Income 65+*† 40 21 120 60+* 38 21 114
HH Age 50.2+* 49 36 77 50.4+* 49 36 76
HH Educ 13.1+*† 13 12 17 13.3+* 13 12 17
HH Net Worth 345+*† 95 10 693 289+* 57 6 606
Homeowner 0.61+* 0.60+*
Home to Net Worth 0.46+*† 0.43 0 1 0.42+* 0.33 0 1
HH Partnered 0.55+* 0.54+*
HH Cred Access 0.66+*† 0.45+*
Risk Prefs 3.2+*† 3 3 4 3.3+* 4 3 4
Est. SB N=938 N=1306
HH Income 214† 91 58 394 168 75 44 356
Non Bus HH Income 121† 51 13 230 101 48 13 225
HH Age 51.5† 51 43 66 54.0 55.0 46.0 70.0
HH Educ 14.3 14 12 17 14.4 15.0 12.0 17.0
Non Bus Networth 1,433† 652 225 5,723 1,249 529 149 4,225
Net Worth 2,405†(z) 807 284 7,645 1,960 615 190 5,456
Homeowner 0.89 0.88
Home to Networth 0.29† 0.23 0.10 0.71 0.26 0.19 0.05 0.66
HH Partnered 0.78 0.81
HH Cred Access 0.64† 0.73
Risk Prefs 2.7† 3 2 4 2.8 3 2 4
New SB N=199 N=230
HH Income 115* 73 43 186 107*(z) 64 39 193
Non Bus HH Income 91* 54 30 162 93 51 25 180
HH Age 42.6* 41 34 60 42.8* 43 32 60
HH Educ 14.7* 16 13 17 14.6 16 12 17
Non Bus Networth 562*† 243 107 1,394 631* 158 47 1,992
Net Worth 802* 289 123 2,074 855* 215 64 2,362
Homeowner 0.79*† 0.70*
Home to Networth 0.35* 0.25 0.07 1 0.29 0.15 0 1
HH Partnered 0.83 0.77
HH Cred Access 0.70*† 0.63
Risk Prefs 2.7 3 2 4 2.8* 3 2 4 +
Mean significantly different from New SBs at 5% or greater. *Mean significantly different from Established SBs at 5% or greater. †
Mean significantly different from 2010 at 5% or greater. All $ values expressed in thousands. Comparison for income and net worth measures are made in both logs and levels. Log statistical comparisons are made to mitigate the effect of outliers. (z) indicates significant for levels only. (y) indicates significant in logs only.
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Table B1.B: Characteristics of HHs That Own and Actively Managed Small Businesses and Those That Do Not
2007 2010 Mean P50 P25 P90 Mean P50 P25 P90
Non SB Owners N=3135 N=5031
HH Income 65+*† 40 21 120 60+* 38 21 114
HH Age 50.2+* 49 36 77 50.4+* 49 36 76
HH Educ 13.1+*† 13 12 17 13.3+* 13 12 17
HH Net Worth 345+*† 95 10 693 289+* 57 6 606
Homeowner 0.61+* 0.60+*
Home to Networth 0.46+*† 0.43 0 1 0.42+* 0.33 0 1
HH Partnered 0.55+* 0.54+*
HH Cred Access 0.66+*† 0.45+*
Risk Prefs 3.2+*† 3 3 4 3.3+* 4 3 4
No Emp SB N=267 N=358
HH Income 114 74 42 172 103 63 34 201
Non Bus HH Income 82 47 18 135 73 42 12 160
HH Age 48.2† 48 38 63 50.3 51 41 67
HH Educ 14.5 15 13 17 14.2 14 12 17
Non Bus Networth 709† 205 64 1,390 642 153 34 1,709
Net Worth 910†(z) 263 88 1,926 786 217 70 1,971
Homeowner 0.81 0.80
Home to Networth 0.39† 0.32 0.13 1 0.32 0.22 0.01 0.86
HH Partnered 0.74 0.73
HH Cred Access 0.57† 0.70
Risk Prefs 2.8† 3 2 4 3.0 3 2 4
Emp SB N=870 N=948
HH Income 230*† 100 60 450 186* 82 48 395
Non Bus HH Income 132* 56 19 254 117* 54 20 263
HH Age 49.1*† 49 39 65 51.8* 52 43 69
HH Educ 14.3 15 12 17 14.6* 16 12 17
Non Bus Networth 1,474* 379 128 3,506 1,404* 385 87 3,437
Net Worth 2,592*† 699 237 6,402 2,298* 592 154 5,316
Homeowner 0.89* 0.86*
Home to Networth 0.35* 0.20 0.09 0.77 0.26* 0.18 0.06 0.68
HH Partnered 0.83* 0.84*
HH Cred Access 0.72* 0.70
Risk Prefs 2.8† 3 2 4 2.9* 3 2 4 +
Mean significantly different from Emp SBs at 5% or greater. *Mean significantly different from No Emp SBs at 5% or greater. †
Mean significantly different from 2010 at 5% or greater. All $ values expressed in thousands. Comparison for income and net worth measures are made in both logs and levels. Log statistical comparisons are made to mitigate the effect of outliers. (z) indicates significant for levels only. (y) indicates significant in logs only.
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Table B2: Characteristics of HHs That Actively Manage a SB, by Survival Status 2007–2009
2007
Mean Median P25 P90
Survived 07-09 N=923
HH Income 205 90 55 383
Non Bus Income 121 54 17 227
HH Age 49.3 49 40 65
HH Educ 14.5 16 12 17
Non Bus Networth 2,237 557 198 5,514
Home to Networth 0.31 0.23 0.10 0.92
Partnered 0.82
HH Credit Access 0.89
Homeowner 0.88
Risk Prefs 2.78 3 2 4
Failed 07-09 N=65
HH Income 86* 63 48 147
Non Bus Income 70* 50 30 135
HH Age 45.9 42 35 65
HH Educ 14.6 15 12 17
Non Bus Networth 515* 180 34 921
Home to Networth 0.40 0.32 0.10 1
Partnered 0.74
HH Credit Access 0.89
Homeowner 0.70*
Risk Prefs 2.92 3 2 4
*Mean significantly different from Open SBs at 5% or greater.
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Table B3: Characteristics of 2007 Primary SBs Actively Managed by HHs, by Survival Status 2007–2009
2007
Mean Median P25 P90
Survived 07-09
# Employees 8.47 1 0 14
Bus Income 501 30 5 500
Bus Sales 1,912 92 23 1,800
Bus Value 2,717 102 14 4,000
Business Age 12.1 9 3 28
HH Bus Loan 0.17
Amt (% of Sales, given loan)
1.19 0.21 0.033 1.88
Failed 07-09
# Employees 1.74+ 0 0 2
Bus Income 28+ 2 0 68
Bus Sales 79+ 9 1 200
Bus Value 120+ 10 0.2 331
Business Age 7.5+ 4 1 20
HH Bus Loan 0.15+
Amt (% of Sales, given loan)
5.52+ 1.32 0.6 8.33
+Mean significantly different from HHs with Open SBs at 95 % or greater.
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Table B4: Surviving vs Failed 2007 SBs by Industry
% of Primary SBs Actively Managed by a HH
2007
Surviving SB
Agricultural 6.4
Mining 18.6
Manufacturing 7.1
Wholesale/Retail 14.2
Lower Tech Service 12.7
Prof Services 41.0
Failing SB
Agricultural 4.0
Mining 14.9
Manufacturing 3.2
Wholesale/Retail 20.0
Lower Tech Service 21.7
Prof Services 36.1
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Table B5: Surviving vs Failed 2007 SBs by Ownership Structure
2007
Survived 07-09
Sole Proprietor 43.4
Subchapter S 17.9
LLC/LLP 16.7
Partnership 12.5
Other 9.3
Failed 07-09
Sole Proprietor 63.2
Subchapter S 4.6
LLC/LLP 21.1
Partnership 6.8
Other 4.2
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Table B6: Characteristics of HHs That Started a SB During Crisis 2007–2009
2007
Mean Median P25 P90
Started SB 07-09 N=131
Income 97* 70 43 163
HH Age 45.2* 46 34 63
HH Educ 14.5* 15 12 17
Networth 641* 166 39 1,398
Home to Networth 0.46 0.39 0.09 1
Partnered 0.69*
HH Credit Access 0.56*
Risk Prefs 2.84* 3 2 4
Unemp 12 Mo 2007 0.11
Unemp 12 Mo 2009 0.29*
Did Not Start SB 07-09 N=2464
Income 60 40 22 116
HH Age 50 48 35 75
HH Educ 13.1 13 12 16
Networth 294 88 10 657
Home to Networth 0.47 0.46 0 1
Partnered 0.55
HH Credit Access 0.43
Risk Prefs 3.2 3 3 4
Unemp 12 Mo 2007 0.11
Unemp 12 Mo 2009 0.16
*Mean significantly different from HH that did not start an SB 2007-2009 at 5% or greater.
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Table B7: Employer Small Businesses, by Industrial Category, Percent, 2007
Industry SUSB SCF
Forestry, Fishing, Hunting, and Agriculture Support 0.4 3.1 Mining 0.3 0.2 Utilities 0.1 0.2 Construction 13.1 19.4 Manufacturing 4.7 5.9 Wholesale Trade 5.5 3.5 Retail Trade 11.7 7.6 Transportation and Warehousing 2.8 2.4 Information 1.2 1.6 Finance and Insurance 4.3 4.5 Real Estate and Rental and Leasing 4.9 6.1 Professional, Scientific, and Technical Services 12.9 17.6 Management of Companies and Enterprises 0.3 0.0 Administrative and Support and Waste Management and Remediation Services 5.3 7.1 Educational Services 1.2 1.1 Health Care and Social Assistance 10.1 6.3 Arts, Entertainment, and Recreation 1.9 2.2 Accommodation and Food Services 7.8 4.1 Other Services (except Public Administration) 11.1 7.3 Unclassified 0.2 0.0
Table B8: Employer small businesses, by number of employees, percent, 2007
Number of employees SUSB SCF
<5 61.4 58.7
5-9 17.6 16.9
10-19 10.7 11.6
20-99 8.8 9.9
100-499 1.5 3.0